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A METHOD OF CONTENT-BASED IMAGE RETRIEVAL FOR THE GENERATION OF IMAGE MOSAICS
Title
A METHOD OF CONTENT-BASED IMAGE RETRIEVAL FOR THE GENERATION OF IMAGE MOSAICS.
Creator
Snead, Michael, Richie, Samuel, University of Central Florida
Abstract / Description
An image mosaic is an artistic work that uses a number of smaller images creatively combined together to form another larger image. Each building block image, or tessera, has its own distinctive and meaningful content, but when viewed from a distance the tesserae come together to form an aesthetically pleasing montage. This work presents the design and implementation of MosaiX, a computer software system that generates these image mosaics automatically. To control the image mosaic creation...
Show moreAn image mosaic is an artistic work that uses a number of smaller images creatively combined together to form another larger image. Each building block image, or tessera, has its own distinctive and meaningful content, but when viewed from a distance the tesserae come together to form an aesthetically pleasing montage. This work presents the design and implementation of MosaiX, a computer software system that generates these image mosaics automatically. To control the image mosaic creation process, several parameters are used within the system. Each parameter affects the overall mosaic quality, as well as required processing time, in its own unique way. A detailed analysis is performed to evaluate each parameter individually. Additionally, this work proposes two novel ways by which to evaluate the quality of an image mosaic in a quantitative way. One method focuses on the perceptual color accuracy of the mosaic reproduction, while the other concentrates on edge replication. Both measures include preprocessing to take into account the unique visual features present in an image mosaic. Doing so minimizes quality penalization due the inherent properties of an image mosaic that make them visually appealing.
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Date Issued
2007
Identifier
CFE0001585, ucf:47115
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001585
Techniques for boosting the performance in Content-Based Image Retrieval Systems
Title
Techniques for boosting the performance in Content-Based Image Retrieval Systems.
Creator
Yu, Ning, Hua, Kien, Hughes, Charles, Dutton, Ronald, Wang, Chung-Ching, University of Central Florida
Abstract / Description
Content-Based Image Retrieval has been an active research area for decades. In a CBIR system, one or more images are used as query to search for similar images. The similarity is measured on the low level features, such as color, shape, edge, texture. First, each image is processed and visual features are extracted. Therefore each image becomes a point in the feature space. Then, if two images are close to each other in the feature space, they are considered similar. That is, the k nearest...
Show moreContent-Based Image Retrieval has been an active research area for decades. In a CBIR system, one or more images are used as query to search for similar images. The similarity is measured on the low level features, such as color, shape, edge, texture. First, each image is processed and visual features are extracted. Therefore each image becomes a point in the feature space. Then, if two images are close to each other in the feature space, they are considered similar. That is, the k nearest neighbors are considered the most similar images to the query image. In this K-Nearest Neighbor (k-NN) model, semantically similar images are assumed to be clustered together in a single neighborhood in the high-dimensional feature space. Unfortunately semantically similar images with different appearances are often clustered into distinct neighborhoods, which might scatter in the feature space. Hence, confinement of the search results to a single neighborhood is the latent reason of the low recall rate of typical nearest neighbor techniques. In this dissertation, a new image retrieval technique - the Query Decomposition (QD) model is introduced. QD facilitates retrieval of semantically similar images from multiple neighborhoods in the feature space and hence bridges the semantic gap between the images' low-level feature and the high-level semantic meaning. In the QD model, a query may be decomposed into multiple subqueries based on the user's relevance feedback to cover multiple image clusters which contain semantically similar images. The retrieval results are the k most similar images from multiple discontinuous relevant clusters. To apply the benefit from QD study, a mobile client-side relevance feedback study was conducted. With the proliferation of handheld devices, the demand of multimedia information retrieval on mobile devices has attracted more attention. A relevance feedback information retrieval process usually includes several rounds of query refinement. Each round incurs exchange of tens of images between the mobile device and the server. With limited wireless bandwidth, this process can incur substantial delay making the system unfriendly to use. The Relevance Feedback Support (RFS) structure that was designed in QD technique was adopted for Client-side Relevance Feedback (CRF). Since relevance feedback is done on client side, system response is instantaneous significantly enhancing system usability. Furthermore, since the server is not involved in relevance feedback processing, it is able to support thousands more users simultaneously. As the QD technique improves on the accuracy of CBIR systems, another study, which is called In-Memory relevance feedback is studied in this dissertation. In the study, we improved the efficiency of the CBIR systems. Current methods rely on searching the database, stored on disks, in each round of relevance feedback. This strategy incurs long delay making relevance feedback less friendly to the user, especially for very large databases. Thus, scalability is a limitation of existing solutions. The proposed in-memory relevance feedback technique substantially reduce the delay associated with feedback processing, and therefore improve system usability. A data-independent dimensionality-reduction technique is used to compress the metadata to build a small in-memory database to support relevance feedback operations with minimal disk accesses. The performance of this approach is compared with conventional relevance feedback techniques in terms of computation efficiency and retrieval accuracy. The results indicate that the new technique substantially reduces response time for user feedback while maintaining the quality of the retrieval. In the previous studies, the QD technique relies on a pre-defined Relevance SupportSupport structure. As the result and user experience indicated that the structure might confine the search range and affect the result. In this dissertation, a novel Multiple Direction Search framework for semi-automatic annotation propagation is studied. In this system, the user interacts with the system to provide example images and the corresponding annotations during the annotation propagation process. In each iteration, the example images are dynamically clustered and the corresponding annotations are propagated separately to each cluster: images in the local neighborhood are annotated. Furthermore, some of those images are returned to the user for further annotation. As the user marks more images, the annotation process goes into multiple directions in the feature space. The query movements can be treated as multiple path navigation. Each path could be further split based on the user's input. In this manner, the system provides accurate annotation assistance to the user - images with the same semantic meaning but different visual characteristics can be handled effectively. From comprehensive experiments on Corel and U. of Washington image databases, the proposed technique shows accuracy and efficiency on annotating image databases.
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Date Issued
2011
Identifier
CFE0004182, ucf:49058
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004182
Understanding images and videos using context
Title
Understanding images and videos using context.
Creator
Vaca Castano, Gonzalo, Da Vitoria Lobo, Niels, Shah, Mubarak, Mikhael, Wasfy, Jones, W Linwood, Wiegand, Rudolf, University of Central Florida
Abstract / Description
In computer vision, context refers to any information that may influence how visual media are understood.(&)nbsp; Traditionally, researchers have studied the influence of several sources of context in relation to the object detection problem in images. In this dissertation, we present a multifaceted review of the problem of context.(&)nbsp; Context is analyzed as a source of improvement in the object detection problem, not only in images but also in videos. In the case of images, we also...
Show moreIn computer vision, context refers to any information that may influence how visual media are understood.(&)nbsp; Traditionally, researchers have studied the influence of several sources of context in relation to the object detection problem in images. In this dissertation, we present a multifaceted review of the problem of context.(&)nbsp; Context is analyzed as a source of improvement in the object detection problem, not only in images but also in videos. In the case of images, we also investigate the influence of the semantic context, determined by objects, relationships, locations, and global composition, to achieve a general understanding of the image content as a whole. In our research, we also attempt to solve the related problem of finding the context associated with visual media. Given a set of visual elements (images), we want to extract the context that can be commonly associated with these images in order to remove ambiguity. The first part of this dissertation concentrates on achieving image understanding using semantic context.(&)nbsp; In spite of the recent success in tasks such as image classi?cation, object detection, image segmentation, and the progress on scene understanding, researchers still lack clarity about computer comprehension of the content of the image as a whole. Hence, we propose a Top-Down Visual Tree (TDVT) image representation that allows the encoding of the content of the image as a hierarchy of objects capturing their importance, co-occurrences, and type of relations. A novel Top-Down Tree LSTM network is presented to learn about the image composition from the training images and their TDVT representations. Given a test image, our algorithm detects objects and determine the hierarchical structure that they form, encoded as a TDVT representation of the image.A single image could have multiple interpretations that may lead to ambiguity about the intentionality of an image.(&)nbsp; What if instead of having only a single image to be interpreted, we have multiple images that represent the same topic. The second part of this dissertation covers how to extract the context information shared by multiple images. We present a method to determine the topic that these images represent. We accomplish this task by transferring tags from an image retrieval database, and by performing operations in the textual space of these tags. As an application, we also present a new image retrieval method that uses multiple images as input. Unlike earlier works that focus either on using just a single query image or using multiple query images with views of the same instance, the new image search paradigm retrieves images based on the underlying concepts that the input images represent.Finally, in the third part of this dissertation, we analyze the influence of context in videos. In this case, the temporal context is utilized to improve scene identification and object detection. We focus on egocentric videos, where agents require some time to change from one location to another. Therefore, we propose a Conditional Random Field (CRF) formulation, which penalizes short-term changes of the scene identity to improve the scene identity accuracy.(&)nbsp; We also show how to improve the object detection outcome by re-scoring the results based on the scene identity of the tested frame. We present a Support Vector Regression (SVR) formulation in the case that explicit knowledge of the scene identity is available during training time. In the case that explicit scene labeling is not available, we propose an LSTM formulation that considers the general appearance of the frame to re-score the object detectors.
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Date Issued
2017
Identifier
CFE0006922, ucf:51703
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006922
Hashing for Multimedia Similarity Modeling and Large-Scale Retrieval
Title
Hashing for Multimedia Similarity Modeling and Large-Scale Retrieval.
Creator
Li, Kai, Hua, Kien, Qi, GuoJun, Hu, Haiyan, Wang, Chung-Ching, University of Central Florida
Abstract / Description
In recent years, the amount of multimedia data such as images, texts, and videos have been growing rapidly on the Internet. Motivated by such trends, this thesis is dedicated to exploiting hashing-based solutions to reveal multimedia data correlations and support intra-media and inter-media similarity search among huge volumes of multimedia data.We start by investigating a hashing-based solution for audio-visual similarity modeling and apply it to the audio-visual sound source localization...
Show moreIn recent years, the amount of multimedia data such as images, texts, and videos have been growing rapidly on the Internet. Motivated by such trends, this thesis is dedicated to exploiting hashing-based solutions to reveal multimedia data correlations and support intra-media and inter-media similarity search among huge volumes of multimedia data.We start by investigating a hashing-based solution for audio-visual similarity modeling and apply it to the audio-visual sound source localization problem. We show that synchronized signals in audio and visual modalities demonstrate similar temporal changing patterns in certain feature spaces. We propose to use a permutation-based random hashing technique to capture the temporal order dynamics of audio and visual features by hashing them along the temporal axis into a common Hamming space. In this way, the audio-visual correlation problem is transformed into a similarity search problem in the Hamming space. Our hashing-based audio-visual similarity modeling has shown superior performances in the localization and segmentation of sounding objects in videos.The success of the permutation-based hashing method motivates us to generalize and formally define the supervised ranking-based hashing problem, and study its application to large-scale image retrieval. Specifically, we propose an effective supervised learning procedure to learn optimized ranking-based hash functions that can be used for large-scale similarity search. Compared with the randomized version, the optimized ranking-based hash codes are much more compact and discriminative. Moreover, it can be easily extended to kernel space to discover more complex ranking structures that cannot be revealed in linear subspaces. Experiments on large image datasets demonstrate the effectiveness of the proposed method for image retrieval.We further studied the ranking-based hashing method for the cross-media similarity search problem. Specifically, we propose two optimization methods to jointly learn two groups of linear subspaces, one for each media type, so that features' ranking orders in different linear subspaces maximally preserve the cross-media similarities. Additionally, we develop this ranking-based hashing method in the cross-media context into a flexible hashing framework with a more general solution. We have demonstrated through extensive experiments on several real-world datasets that the proposed cross-media hashing method can achieve superior cross-media retrieval performances against several state-of-the-art algorithms.Lastly, to make better use of the supervisory label information, as well as to further improve the efficiency and accuracy of supervised hashing, we propose a novel multimedia discrete hashing framework that optimizes an instance-wise loss objective, as compared to the pairwise losses, using an efficient discrete optimization method. In addition, the proposed method decouples the binary codes learning and hash function learning into two separate stages, thus making the proposed method equally applicable for both single-media and cross-media search. Extensive experiments on both single-media and cross-media retrieval tasks demonstrate the effectiveness of the proposed method.
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Date Issued
2017
Identifier
CFE0006759, ucf:51840
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006759
Video categorization using semantics and semiotics
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.
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Date Issued
2003
Identifier
CFR0001717, ucf:52920
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFR0001717
EFFICIENT TECHNIQUES FOR RELEVANCE FEEDBACK PROCESSING IN CONTENT-BASED IMAGE RETRIEVAL
Title
EFFICIENT TECHNIQUES FOR RELEVANCE FEEDBACK PROCESSING IN CONTENT-BASED IMAGE RETRIEVAL.
Creator
Liu, Danzhou, Hua, Kien, University of Central Florida
Abstract / Description
In content-based image retrieval (CBIR) systems, there are two general types of search: target search and category search. Unlike queries in traditional database systems, users in most cases cannot specify an ideal query to retrieve the desired results for either target search or category search in multimedia database systems, and have to rely on iterative feedback to refine their query. Efficient evaluation of such iterative queries can be a challenge, especially when the multimedia database...
Show moreIn content-based image retrieval (CBIR) systems, there are two general types of search: target search and category search. Unlike queries in traditional database systems, users in most cases cannot specify an ideal query to retrieve the desired results for either target search or category search in multimedia database systems, and have to rely on iterative feedback to refine their query. Efficient evaluation of such iterative queries can be a challenge, especially when the multimedia database contains a large number of entries, and the search needs many iterations, and when the underlying distance measure is computationally expensive. The overall processing costs, including CPU and disk I/O, are further emphasized if there are numerous concurrent accesses. To address these limitations involved in relevance feedback processing, we propose a generic framework, including a query model, index structures, and query optimization techniques. Specifically, this thesis has five main contributions as follows. The first contribution is an efficient target search technique. We propose four target search methods: naive random scan (NRS), local neighboring movement (LNM), neighboring divide-and-conquer (NDC), and global divide-and-conquer (GDC) methods. All these methods are built around a common strategy: they do not retrieve checked images (i.e., shrink the search space). Furthermore, NDC and GDC exploit Voronoi diagrams to aggressively prune the search space and move towards target images. We theoretically and experimentally prove that the convergence speeds of GDC and NDC are much faster than those of NRS and recent methods. The second contribution is a method to reduce the number of expensive distance computation when answering k-NN queries with non-metric distance measures. We propose an efficient distance mapping function that transfers non-metric measures into metric, and still preserves the original distance orderings. Then existing metric index structures (e.g., M-tree) can be used to reduce the computational cost by exploiting the triangular inequality property. The third contribution is an incremental query processing technique for Support Vector Machines (SVMs). SVMs have been widely used in multimedia retrieval to learn a concept in order to find the best matches. SVMs, however, suffer from the scalability problem associated with larger database sizes. To address this limitation, we propose an efficient query evaluation technique by employing incremental update. The proposed technique also takes advantage of a tuned index structure to efficiently prune irrelevant data. As a result, only a small portion of the data set needs to be accessed for query processing. This index structure also provides an inexpensive means to process the set of candidates to evaluate the final query result. This technique can work with different kernel functions and kernel parameters. The fourth contribution is a method to avoid local optimum traps. Existing CBIR systems, designed around query refinement based on relevance feedback, suffer from local optimum traps that may severely impair the overall retrieval performance. We therefore propose a simulated annealing-based approach to address this important issue. When a stuck-at-a-local-optimum occurs, we employ a neighborhood search technique (i.e., simulated annealing) to continue the search for additional matching images, thus escaping from the local optimum. We also propose an index structure to speed up such neighborhood search. Finally, the fifth contribution is a generic framework to support concurrent accesses. We develop new storage and query processing techniques to exploit sequential access and leverage inter-query concurrency to share computation. Our experimental results, based on the Corel dataset, indicate that the proposed optimization can significantly reduce average response time while achieving better precision and recall, and is scalable to support a large user community. This latter performance characteristic is largely neglected in existing systems making them less suitable for large-scale deployment. With the growing interest in Internet-scale image search applications, our framework offers an effective solution to the scalability problem.
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Date Issued
2009
Identifier
CFE0002728, ucf:48162
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002728