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- Title
- TRANSFORM BASED AND SEARCH AWARE TEXT COMPRESSION SCHEMES AND COMPRESSED DOMAIN TEXT RETRIEVAL.
- Creator
-
Zhang, Nan, Mukherjee, Amar, University of Central Florida
- Abstract / Description
-
In recent times, we have witnessed an unprecedented growth of textual information via the Internet, digital libraries and archival text in many applications. While a good fraction of this information is of transient interest, useful information of archival value will continue to accumulate. We need ways to manage, organize and transport this data from one point to the other on data communications links with limited bandwidth. We must also have means to speedily find the information we need...
Show moreIn recent times, we have witnessed an unprecedented growth of textual information via the Internet, digital libraries and archival text in many applications. While a good fraction of this information is of transient interest, useful information of archival value will continue to accumulate. We need ways to manage, organize and transport this data from one point to the other on data communications links with limited bandwidth. We must also have means to speedily find the information we need from this huge mass of data. Sometimes, a single site may also contain large collections of data such as a library database, thereby requiring an efficient search mechanism even to search within the local data. To facilitate the information retrieval, an emerging ad hoc standard for uncompressed text is XML which preprocesses the text by putting additional user defined metadata such as DTD or hyperlinks to enable searching with better efficiency and effectiveness. This increases the file size considerably, underscoring the importance of applying text compression. On account of efficiency (in terms of both space and time), there is a need to keep the data in compressed form for as much as possible. (2) Exact and approximate pattern matching in Burrows-Wheeler transformed (BWT) files: We proposed a method to extract the useful context information in linear time from the BWT transformed text. The auxiliary arrays obtained from BWT inverse transform brings logarithm search time. Meanwhile, approximate pattern matching can be performed based on the results of exact pattern matching to extract the possible candidate for the approximate pattern matching. Then fast verifying algorithm can be applied to those candidates which could be just small parts of the original text. We present algorithms for both k-mismatch and k-approximate pattern matching in BWT compressed text. A typical compression system based on BWT has Move-to-Front and Huffman coding stages after the transformation. We propose a novel approach to replace the Move-to-Front stage in order to extend compressed domain search capability all the way to the entropy coding stage. A modification to the Move-to-Front makes it possible to randomly access any part of the compressed text without referring to the part before the access point. . Modified LZW algorithm that allows random access and partial decoding for the compressed text retrieval: Although many compression algorithms provide good compression ratio and/or time complexity, LZW is the first one studied for the compressed pattern matching because of its simplicity and efficiency. Modifications on LZW algorithm provide the extra advantage for fast random access and partial decoding ability that is especially useful for text retrieval systems. Based on this algorithm, we can provide a dynamic hierarchical semantic structure for the text, so that the text search can be performed on the expected level of granularity. For example, user can choose to retrieve a single line, a paragraph, or a file, etc. that contains the keywords. More importantly, we will show that parallel encoding and decoding algorithm is trivial with the modified LZW. Both encoding and decoding can be performed with multiple processors easily and encoding and decoding process are independent with respect to the number of processors. Text compression is concerned with techniques for representing the digital text data in alternate representations that takes less space. Not only does it help conserve the storage space for archival and online data, it also helps system performance by requiring less number of secondary storage (disk or CD Rom) accesses and improves the network transmission bandwidth utilization by reducing the transmission time. Unlike static images or video, there is no international standard for text compression, although compressed formats like .zip, .gz, .Z files are increasingly being used. In general, data compression methods are classified as lossless or lossy. Lossless compression allows the original data to be recovered exactly. Although used primarily for text data, lossless compression algorithms are useful in special classes of images such as medical imaging, finger print data, astronomical images and data bases containing mostly vital numerical data, tables and text information. Many lossy algorithms use lossless methods at the final stage of the encoding stage underscoring the importance of lossless methods for both lossy and lossless compression applications. In order to be able to effectively utilize the full potential of compression techniques for the future retrieval systems, we need efficient information retrieval in the compressed domain. This means that techniques must be developed to search the compressed text without decompression or only with partial decompression independent of whether the search is done on the text or on some inversion table corresponding to a set of key words for the text. In this dissertation, we make the following contributions: (1) Star family compression algorithms: We have proposed an approach to develop a reversible transformation that can be applied to a source text that improves existing algorithm's ability to compress. We use a static dictionary to convert the English words into predefined symbol sequences. These transformed sequences create additional context information that is superior to the original text. Thus we achieve some compression at the preprocessing stage. We have a series of transforms which improve the performance. Star transform requires a static dictionary for a certain size. To avoid the considerable complexity of conversion, we employ the ternary tree data structure that efficiently converts the words in the text to the words in the star dictionary in linear time. (2) Exact and approximate pattern matching in Burrows-Wheeler transformed (BWT) files: We proposed a method to extract the useful context information in linear time from the BWT transformed text. The auxiliary arrays obtained from BWT inverse transform brings logarithm search time. Meanwhile, approximate pattern matching can be performed based on the results of exact pattern matching to extract the possible candidate for the approximate pattern matching. Then fast verifying algorithm can be applied to those candidates which could be just small parts of the original text. We present algorithms for both k-mismatch and k-approximate pattern matching in BWT compressed text. A typical compression system based on BWT has Move-to-Front and Huffman coding stages after the transformation. We propose a novel approach to replace the Move-to-Front stage in order to extend compressed domain search capability all the way to the entropy coding stage. A modification to the Move-to-Front makes it possible to randomly access any part of the compressed text without referring to the part before the access point. (3) Modified LZW algorithm that allows random access and partial decoding for the compressed text retrieval: Although many compression algorithms provide good compression ratio and/or time complexity, LZW is the first one studied for the compressed pattern matching because of its simplicity and efficiency. Modifications on LZW algorithm provide the extra advantage for fast random access and partial decoding ability that is especially useful for text retrieval systems. Based on this algorithm, we can provide a dynamic hierarchical semantic structure for the text, so that the text search can be performed on the expected level of granularity. For example, user can choose to retrieve a single line, a paragraph, or a file, etc. that contains the keywords. More importantly, we will show that parallel encoding and decoding algorithm is trivial with the modified LZW. Both encoding and decoding can be performed with multiple processors easily and encoding and decoding process are independent with respect to the number of processors.
Show less - Date Issued
- 2005
- Identifier
- CFE0000438, ucf:46396
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000438
- Title
- TRANSFORM BASED AND SEARCH AWARE TEXT COMPRESSION SCHEMES AND COMPRESSED DOMAIN TEXT RETRIEVAL.
- Creator
-
Zhang, Nan, Mukherjee, Amar, University of Central Florida
- Abstract / Description
-
In recent times, we have witnessed an unprecedented growth of textual information via the Internet, digital libraries and archival text in many applications. While a good fraction of this information is of transient interest, useful information of archival value will continue to accumulate. We need ways to manage, organize and transport this data from one point to the other on data communications links with limited bandwidth. We must also have means to speedily find the information we need...
Show moreIn recent times, we have witnessed an unprecedented growth of textual information via the Internet, digital libraries and archival text in many applications. While a good fraction of this information is of transient interest, useful information of archival value will continue to accumulate. We need ways to manage, organize and transport this data from one point to the other on data communications links with limited bandwidth. We must also have means to speedily find the information we need from this huge mass of data. Sometimes, a single site may also contain large collections of data such as a library database, thereby requiring an efficient search mechanism even to search within the local data. To facilitate the information retrieval, an emerging ad hoc standard for uncompressed text is XML which preprocesses the text by putting additional user defined metadata such as DTD or hyperlinks to enable searching with better efficiency and effectiveness. This increases the file size considerably, underscoring the importance of applying text compression. On account of efficiency (in terms of both space and time), there is a need to keep the data in compressed form for as much as possible. Text compression is concerned with techniques for representing the digital text data in alternate representations that takes less space. Not only does it help conserve the storage space for archival and online data, it also helps system performance by requiring less number of secondary storage (disk or CD Rom) accesses and improves the network transmission bandwidth utilization by reducing the transmission time. Unlike static images or video, there is no international standard for text compression, although compressed formats like .zip, .gz, .Z files are increasingly being used. In general, data compression methods are classified as lossless or lossy. Lossless compression allows the original data to be recovered exactly. Although used primarily for text data, lossless compression algorithms are useful in special classes of images such as medical imaging, finger print data, astronomical images and data bases containing mostly vital numerical data, tables and text information. Many lossy algorithms use lossless methods at the final stage of the encoding stage underscoring the importance of lossless methods for both lossy and lossless compression applications. In order to be able to effectively utilize the full potential of compression techniques for the future retrieval systems, we need efficient information retrieval in the compressed domain. This means that techniques must be developed to search the compressed text without decompression or only with partial decompression independent of whether the search is done on the text or on some inversion table corresponding to a set of key words for the text. In this dissertation, we make the following contributions: (1) Star family compression algorithms: We have proposed an approach to develop a reversible transformation that can be applied to a source text that improves existing algorithm's ability to compress. We use a static dictionary to convert the English words into predefined symbol sequences. These transformed sequences create additional context information that is superior to the original text. Thus we achieve some compression at the preprocessing stage. We have a series of transforms which improve the performance. Star transform requires a static dictionary for a certain size. To avoid the considerable complexity of conversion, we employ the ternary tree data structure that efficiently converts the words in the text to the words in the star dictionary in linear time. (2) Exact and approximate pattern matching in Burrows-Wheeler transformed (BWT) files: We proposed a method to extract the useful context information in linear time from the BWT transformed text. The auxiliary arrays obtained from BWT inverse transform brings logarithm search time. Meanwhile, approximate pattern matching can be performed based on the results of exact pattern matching to extract the possible candidate for the approximate pattern matching. Then fast verifying algorithm can be applied to those candidates which could be just small parts of the original text. We present algorithms for both k-mismatch and k-approximate pattern matching in BWT compressed text. A typical compression system based on BWT has Move-to-Front and Huffman coding stages after the transformation. We propose a novel approach to replace the Move-to-Front stage in order to extend compressed domain search capability all the way to the entropy coding stage. A modification to the Move-to-Front makes it possible to randomly access any part of the compressed text without referring to the part before the access point. (3) Modified LZW algorithm that allows random access and partial decoding for the compressed text retrieval: Although many compression algorithms provide good compression ratio and/or time complexity, LZW is the first one studied for the compressed pattern matching because of its simplicity and efficiency. Modifications on LZW algorithm provide the extra advantage for fast random access and partial decoding ability that is especially useful for text retrieval systems. Based on this algorithm, we can provide a dynamic hierarchical semantic structure for the text, so that the text search can be performed on the expected level of granularity. For example, user can choose to retrieve a single line, a paragraph, or a file, etc. that contains the keywords. More importantly, we will show that parallel encoding and decoding algorithm is trivial with the modified LZW. Both encoding and decoding can be performed with multiple processors easily and encoding and decoding process are independent with respect to the number of processors.
Show less - Date Issued
- 2005
- Identifier
- CFE0000488, ucf:46358
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000488
- Title
- Content-based Information Retrieval via Nearest Neighbor Search.
- Creator
-
Huang, Yinjie, Georgiopoulos, Michael, Anagnostopoulos, Georgios, Hu, Haiyan, Sukthankar, Gita, Ni, Liqiang, University of Central Florida
- Abstract / Description
-
Content-based information retrieval (CBIR) has attracted significant interest in the past few years. When given a search query, the search engine will compare the query with all the stored information in the database through nearest neighbor search. Finally, the system will return the most similar items. We contribute to the CBIR research the following: firstly, Distance Metric Learning (DML) is studied to improve retrieval accuracy of nearest neighbor search. Additionally, Hash Function...
Show moreContent-based information retrieval (CBIR) has attracted significant interest in the past few years. When given a search query, the search engine will compare the query with all the stored information in the database through nearest neighbor search. Finally, the system will return the most similar items. We contribute to the CBIR research the following: firstly, Distance Metric Learning (DML) is studied to improve retrieval accuracy of nearest neighbor search. Additionally, Hash Function Learning (HFL) is considered to accelerate the retrieval process.On one hand, a new local metric learning framework is proposed - Reduced-Rank Local Metric Learning (R2LML). By considering a conical combination of Mahalanobis metrics, the proposed method is able to better capture information like data's similarity and location. A regularization to suppress the noise and avoid over-fitting is also incorporated into the formulation. Based on the different methods to infer the weights for the local metric, we considered two frameworks: Transductive Reduced-Rank Local Metric Learning (T-R2LML), which utilizes transductive learning, while Efficient Reduced-Rank Local Metric Learning (E-R2LML)employs a simpler and faster approximated method. Besides, we study the convergence property of the proposed block coordinate descent algorithms for both our frameworks. The extensive experiments show the superiority of our approaches.On the other hand, *Supervised Hash Learning (*SHL), which could be used in supervised, semi-supervised and unsupervised learning scenarios, was proposed in the dissertation. By considering several codewords which could be learned from the data, the proposed method naturally derives to several Support Vector Machine (SVM) problems. After providing an efficient training algorithm, we also study the theoretical generalization bound of the new hashing framework. In the final experiments, *SHL outperforms many other popular hash function learning methods. Additionally, in order to cope with large data sets, we also conducted experiments running on big data using a parallel computing software package, namely LIBSKYLARK.
Show less - Date Issued
- 2016
- Identifier
- CFE0006327, ucf:51544
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006327
- Title
- COMPRESSED PATTERN MATCHING FOR TEXT AND IMAGES.
- Creator
-
Tao, Tao, Mukherjee, Amar, University of Central Florida
- Abstract / Description
-
The amount of information that we are dealing with today is being generated at an ever-increasing rate. On one hand, data compression is needed to efficiently store, organize the data and transport the data over the limited-bandwidth network. On the other hand, efficient information retrieval is needed to speedily find the relevant information from this huge mass of data using available resources. The compressed pattern matching problem can be stated as: given the compressed format of a text...
Show moreThe amount of information that we are dealing with today is being generated at an ever-increasing rate. On one hand, data compression is needed to efficiently store, organize the data and transport the data over the limited-bandwidth network. On the other hand, efficient information retrieval is needed to speedily find the relevant information from this huge mass of data using available resources. The compressed pattern matching problem can be stated as: given the compressed format of a text or an image and a pattern string or a pattern image, report the occurrence(s) of the pattern in the text or image with minimal (or no) decompression. The main advantages of compressed pattern matching versus the naïve decompress-then-search approach are: First, reduced storage cost. Since there is no need to decompress the data or there is only minimal decompression required, the disk space and the memory cost is reduced. Second, less search time. Since the size of the compressed data is smaller than that of the original data, a searching performed on the compressed data will result in a shorter search time. The challenge of efficient compressed pattern matching can be met from two inseparable aspects: First, to utilize effectively the full potential of compression for the information retrieval systems, there is a need to develop search-aware compression algorithms. Second, for data that is compressed using a particular compression technique, regardless whether the compression is search-aware or not, we need to develop efficient searching techniques. This means that techniques must be developed to search the compressed data with no or minimal decompression and with not too much extra cost. Compressed pattern matching algorithms can be categorized as either for text compression or for image compression. Although compressed pattern matching for text compression has been studied for a few years and many publications are available in the literature, there is still room to improve the efficiency in terms of both compression and searching. None of the search engines available today make explicit use of compressed pattern matching. Compressed pattern matching for image compression, on the other hand, has been relatively unexplored. However, it is getting more attention because lossless compression has become more important for the ever-increasing large amount of medical images, satellite images and aerospace photos, which requires the data to be losslessly stored. Developing efficient information retrieval techniques from the losslessly compressed data is therefore a fundamental research challenge. In this dissertation, we have studied compressed pattern matching problem for both text and images. We present a series of novel compressed pattern matching algorithms, which are divided into two major parts. The first major work is done for the popular LZW compression algorithm. The second major work is done for the current lossless image compression standard JPEG-LS. Specifically, our contributions from the first major work are: 1. We have developed an "almost-optimal" compressed pattern matching algorithm that reports all pattern occurrences. An earlier "almost-optimal" algorithm reported in the literature is only capable of detecting the first occurrence of the pattern and the practical performance of the algorithm is not clear. We have implemented our algorithm and provide extensive experimental results measuring the speed of our algorithm. We also developed a faster implementation for so-called "simple patterns". The simple patterns are patterns that no unique symbol appears more than once. The algorithm takes advantage of this property and runs in optimal time. 2. We have developed a novel compressed pattern matching algorithm for multiple patterns using the Aho-Corasick algorithm. The algorithm takes O(mt+n+r) time with O(mt) extra space, where n is the size of the compressed file, m is the total size of all patterns, t is the size of the LZW trie and r is the number of occurrences of the patterns. The algorithm is particularly efficient when being applied on archival search if the archives are compressed with a common LZW trie. All the above algorithms have been implemented and extensive experiments have been conducted to test the performance of our algorithms and to compare with the best existing algorithms. The experimental results show that our compressed pattern matching algorithm for multiple patterns is competitive among the best algorithms and is practically the fastest among all approaches when the number of patterns is not very large. Therefore, our algorithm is preferable for general string matching applications. LZW is one of the most efficient and popular compression algorithms used extensively and both of our algorithms require no modification on the compression algorithm. Our work, therefore, has great economical and market potential Our contributions from the second major work are: 1 We have developed a new global context variation of the JPEG-LS compression algorithm and the corresponding compressed pattern matching algorithm. Comparing to the original JPEG-LS, the global context variation is search-aware and has faster encoding and decoding speeds. The searching algorithm based on the global-context variation requires partial decompression of the compressed image. The experimental results show that it improves the search speed by about 30% comparing to the decompress-then-search approach. Based on our best knowledge, this is the first two-dimensional compressed pattern matching work for the JPEG-LS standard. 2 We have developed a two-pass variation of the JPEG-LS algorithm and the corresponding compressed pattern matching algorithm. The two-pass variation achieves search-awareness through a common compression technique called semi-static dictionary. Comparing to the original algorithm, the compression of the new algorithm is equally well but the encoding takes slightly longer. The searching algorithm based on the two-pass variation requires no decompression at all and therefore works in the fully compressed domain. It runs in time O(nc+mc+nm+m^2) with extra space O(n+m+mc), where n is the number of columns of the image, m is the number of rows and columns of the pattern, nc is the compressed image size and mc is the compressed pattern size. The algorithm is the first known two-dimensional algorithm that works in the fully compressed domain.
Show less - Date Issued
- 2005
- Identifier
- CFE0000471, ucf:46366
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000471
- Title
- INFORMATION RETRIEVAL PERFORMANCE ENHANCEMENT USING THE AVERAGE STANDARD ESTIMATOR AND THE MULTI-CRITERIA DECISION WEIGHTED SET OF PERFORMANCE MEASURES.
- Creator
-
AHRAM, TAREQ, McCauley-Bush, Pamela, University of Central Florida
- Abstract / Description
-
Information retrieval is much more challenging than traditional small document collection retrieval. The main difference is the importance of correlations between related concepts in complex data structures. These structures have been studied by several information retrieval systems. This research began by performing a comprehensive review and comparison of several techniques of matrix dimensionality estimation and their respective effects on enhancing retrieval performance using singular...
Show moreInformation retrieval is much more challenging than traditional small document collection retrieval. The main difference is the importance of correlations between related concepts in complex data structures. These structures have been studied by several information retrieval systems. This research began by performing a comprehensive review and comparison of several techniques of matrix dimensionality estimation and their respective effects on enhancing retrieval performance using singular value decomposition and latent semantic analysis. Two novel techniques have been introduced in this research to enhance intrinsic dimensionality estimation, the Multi-criteria Decision Weighted model to estimate matrix intrinsic dimensionality for large document collections and the Average Standard Estimator (ASE) for estimating data intrinsic dimensionality based on the singular value decomposition (SVD). ASE estimates the level of significance for singular values resulting from the singular value decomposition. ASE assumes that those variables with deep relations have sufficient correlation and that only those relationships with high singular values are significant and should be maintained. Experimental results over all possible dimensions indicated that ASE improved matrix intrinsic dimensionality estimation by including the effect of both singular values magnitude of decrease and random noise distracters. Analysis based on selected performance measures indicates that for each document collection there is a region of lower dimensionalities associated with improved retrieval performance. However, there was clear disagreement between the various performance measures on the model associated with best performance. The introduction of the multi-weighted model and Analytical Hierarchy Processing (AHP) analysis helped in ranking dimensionality estimation techniques and facilitates satisfying overall model goals by leveraging contradicting constrains and satisfying information retrieval priorities. ASE provided the best estimate for MEDLINE intrinsic dimensionality among all other dimensionality estimation techniques, and further, ASE improved precision and relative relevance by 10.2% and 7.4% respectively. AHP analysis indicates that ASE and the weighted model ranked the best among other methods with 30.3% and 20.3% in satisfying overall model goals in MEDLINE and 22.6% and 25.1% for CRANFIELD. The weighted model improved MEDLINE relative relevance by 4.4%, while the scree plot, weighted model, and ASE provided better estimation of data intrinsic dimensionality for CRANFIELD collection than Kaiser-Guttman and Percentage of variance. ASE dimensionality estimation technique provided a better estimation of CISI intrinsic dimensionality than all other tested methods since all methods except ASE tend to underestimate CISI document collection intrinsic dimensionality. ASE improved CISI average relative relevance and average search length by 28.4% and 22.0% respectively. This research provided evidence supporting a system using a weighted multi-criteria performance evaluation technique resulting in better overall performance than a single criteria ranking model. Thus, the weighted multi-criteria model with dimensionality reduction provides a more efficient implementation for information retrieval than using a full rank model.
Show less - Date Issued
- 2008
- Identifier
- CFE0002426, ucf:47747
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002426
- Title
- Video categorization using semantics and semiotics.
- Creator
-
Rasheed, Zeeshan, Shah, Mubarak, Engineering and Computer Science
- Abstract / Description
-
University of Central Florida College of Engineering Thesis; There is a great need to automatically segment, categorize, and annotate video data, and to develop efficient tools for browsing and searching. We believe that the categorization of videos can be achieved by exploring the concepts and meanings of the videos. This task requires bridging the gap between low-level content and high-level concepts (or semantics). Once a relationship is established between the low-level computable...
Show moreUniversity of Central Florida College of Engineering Thesis; There is a great need to automatically segment, categorize, and annotate video data, and to develop efficient tools for browsing and searching. We believe that the categorization of videos can be achieved by exploring the concepts and meanings of the videos. This task requires bridging the gap between low-level content and high-level concepts (or semantics). Once a relationship is established between the low-level computable features of the video and its semantics, .the user would be able to navigate through videos through the use of concepts and ideas (for example, a user could extract only those scenes in an action film that actually contain fights) rat her than sequentially browsing the whole video. However, this relationship must follow the norms of human perception and abide by the rules that are most often followed by the creators (directors) of these videos. These rules are called film grammar in video production literature. Like any natural language, this grammar has several dialects, but it has been acknowledged to be universal. Therefore, the knowledge of film grammar can be exploited effectively for the understanding of films. To interpret an idea using the grammar, we need to first understand the symbols, as in natural languages, and second, understand the rules of combination of these symbols to represent concepts. In order to develop algorithms that exploit this film grammar, it is necessary to relate the symbols of the grammar to computable video features.
Show less - Date Issued
- 2003
- Identifier
- CFR0001717, ucf:52920
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFR0001717
- Title
- Facilitating Information Retrieval in Social Media User Interfaces.
- Creator
-
Costello, Anthony, Tang, Yubo, Fiore, Stephen, Goldiez, Brian, University of Central Florida
- Abstract / Description
-
As the amount of computer mediated information (e.g., emails, documents, multi-media) we need to process grows, our need to rapidly sort, organize and store electronic information likewise increases. In order to store information effectively, we must find ways to sort through it and organize it in a manner that facilitates efficient retrieval. The instantaneous and emergent nature of communications across networks like Twitter makes them suitable for discussing events (e.g., natural disasters...
Show moreAs the amount of computer mediated information (e.g., emails, documents, multi-media) we need to process grows, our need to rapidly sort, organize and store electronic information likewise increases. In order to store information effectively, we must find ways to sort through it and organize it in a manner that facilitates efficient retrieval. The instantaneous and emergent nature of communications across networks like Twitter makes them suitable for discussing events (e.g., natural disasters) that are amorphous and prone to rapid changes. It can be difficult for an individual human to filter through and organize the large amounts of information that can pass through these types of social networks when events are unfolding rapidly. A common feature of social networks is the images (e.g., human faces, inanimate objects) that are often used by those who send messages across these networks. Humans have a particularly strong ability to recognize and differentiate between human Faces. This effect may also extend to recalling information associated with each human Face. This study investigated the difference between human Face images, non-human Face images and alphanumeric labels as retrieval cues under different levels of Task Load. Participants were required to recall key pieces of event information as they emerged from a Twitter-style message feed during a simulated natural disaster. A counter-balanced within-subjects design was used for this experiment. Participants were exposed to low, medium and high Task Load while responding to five different types of recall cues: (1) Nickname, (2) Non-Face, (3) Non-Face (&) Nickname, (4) Face and (5) Face (&) Nickname. The task required participants to organize information regarding emergencies (e.g., car accidents) from a Twitter-style message feed. The messages reported various events such as fires occurring around a fictional city. Each message was associated with a different recall cue type, depending on the experimental condition. Following the task, participants were asked to recall the information associated with one of the cues they worked with during the task. Results indicate that under medium and high Task Load, both Non-Face and Face retrieval cues increased recall performance over Nickname alone with Non-Faces resulting in the highest mean recall scores. When comparing medium to high Task Load: Face (&) Nickname and Non-Face significantly outperformed the Face condition. The performance in Non-Face (&) Nickname was significantly better than Face (&) Nickname. No significant difference was found between Non-Faces and Non-Faces (&) Nickname. Subjective Task Load scores indicate that participants experienced lower mental workload when using Non-Face cues than using Nickname or Face cues. Generally, these results indicate that under medium and high Task Load levels, images outperformed alphanumeric nicknames, Non-Face images outperformed Face images, and combining alphanumeric nicknames with images may have offered a significant performance advantage only when the image is that of a Face. Both theoretical and practical design implications are provided from these findings.
Show less - Date Issued
- 2014
- Identifier
- CFE0005318, ucf:50524
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005318
