Bio: Khanh Vu is an academic researcher from University of Central Florida. The author has contributed to research in topics: Image retrieval & Search engine indexing. The author has an hindex of 13, co-authored 38 publications receiving 712 citations. Previous affiliations of Khanh Vu include University of Illinois at Urbana–Champaign & Oklahoma State University–Tulsa.
••14 Dec 1998
TL;DR: The notion of patching window is introduced, and the optimal technique outperforms the existing schemes by a significant margin, and is also up to two times better than the best Piggybacking method which provides data sharing by merging the services in progress into a single stream by altering their display rates.
Abstract: Patching has been shown to be cost efficient for video-on- demand systems. Unlike conventional multicast, patching is a dynamic multicast scheme which enables a new request to join an ongoing multicast. Since a multicast can now grow dynamically to serve new users, this approach is more efficiency than traditional multicast. In addition, since a new request can be serviced immediately without having to wait for the next multicast, true video-on-demand can be achieved. In this paper, we introduce the notion of patching window, and present a generalized patching method. We show that existing schemes are special cases with a specific patching window size. We derive a mathematical formula to help determine the optimal size for the patching window. This formula allows us to design the best patching scheme given a workload. The proposed technique is validated using simulations. They show that the analytical results are very accurate. We also provide performance results to demonstrate that the optimal technique outperforms the existing schemes by a significant margin. It is also up to two times better than the best Piggybacking method which provides data sharing by merging the services in progress into a single stream by altering their display rates.
TL;DR: The experimental results confirm that traditional approaches, such as Local Color Histogram and Correlogram, suffer from the involvement of irrelevant regions and suggest that the proposed method can handle ROI queries and provide significantly better performance.
Abstract: Query-by-example is the most popular query model in recent content-based image retrieval (CBIR) systems. A typical query image includes relevant objects (e.g., Eiffel Tower), but also irrelevant image areas (including background). The irrelevant areas limit the effectiveness of existing CBIR systems. To overcome this limitation, the system must be able to determine similarity based on relevant regions alone. We call this class of queries region-of-interest (ROI) queries and propose a technique for processing them in a sampling-based matching framework. A new similarity model is presented and an indexing technique for this new environment is proposed. Our experimental results confirm that traditional approaches, such as Local Color Histogram and Correlogram, suffer from the involvement of irrelevant regions. Our method can handle ROI queries and provide significantly better performance. We also assessed the performance of the proposed indexing technique. The results clearly show that our retrieval procedure is effective for large image data sets.
••01 Aug 2008
TL;DR: The theoretical analysis shows that the probability of points being assigned to the correct clusters is much higher by the new algorithm, compared to the conventional methods, indicating that the design produces clusters which are closer to the ground truth than clusters created by the current state of theart algorithms.
Abstract: Data clustering is a difficult problem due to the complex and heterogeneous natures of multidimensional data. To improve clustering accuracy, we propose a scheme to capture the local correlation structures: associate each cluster with an independent weighting vector and embed it in the subspace spanned by an adaptive combination of the dimensions. Our clustering algorithm takes advantage of the known pairwise instance-level constraints. The data points in the constraint set are divided into groups through inference; and each group is assigned to the feasible cluster which minimizes the sum of squared distances between all the points in the group and the corresponding centroid. Our theoretical analysis shows that the probability of points being assigned to the correct clusters is much higher by the new algorithm, compared to the conventional methods. This is confirmed by our experimental results, indicating that our design indeed produces clusters which are closer to the ground truth than clusters created by the current state-of-the-art algorithms.
TL;DR: This work proposes a new index structure and query processing technique to improve retrieval effectiveness and efficiency and considers strategies to minimize the effects of users' inaccurate relevance feedback.
Abstract: Target search in content-based image retrieval (CBIR) systems refers to finding a specific (target) image such as a particular registered logo or a specific historical photograph. Existing techniques, designed around query refinement based on relevance feedback, suffer from slow convergence, and do not guarantee to find intended targets. To address these limitations, we propose several efficient query point movement methods. We prove that our approach is able to reach any given target image with fewer iterations in the worst and average cases. We propose a new index structure and query processing technique to improve retrieval effectiveness and efficiency. We also consider strategies to minimize the effects of users' inaccurate relevance feedback. Extensive experiments in simulated and realistic environments show that our approach significantly reduces the number of required iterations and improves overall retrieval performance. The experimental results also confirm that our approach can always retrieve intended targets even with poor selection of initial query points.
30 Oct 1999
TL;DR: This paper proposes a new image retrieval technique that allows users to control the relevantness of the results and shows that this technique is not only space-time efficient but also more effective than recently proposed color histogram techniques.
Abstract: The rapid growth of digital image data increases the need for efficient and effective image retrieval systems. Such systems should provide functionality that tailors to the user's need at the query time. In this paper, we propose a new image retrieval technique that allows users to control the relevantness of the results. For each image, the color contents of its regions are captured and used to compute similarity. Various factors, assigned automatically or by the user, allow high recall and precision to be obtained. We implemented the proposed technique for a large database of 16,000 images. Our experimental results show that this technique is not only space-time efficient but also more effective than recently proposed color histogram techniques.
TL;DR: This paper attempts to provide a comprehensive survey of the recent technical achievements in high-level semantic-based image retrieval, identifying five major categories of the state-of-the-art techniques in narrowing down the 'semantic gap'.
Abstract: In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the 'semantic gap' between the visual features and the richness of human semantics. This paper attempts to provide a comprehensive survey of the recent technical achievements in high-level semantic-based image retrieval. Major recent publications are included in this survey covering different aspects of the research in this area, including low-level image feature extraction, similarity measurement, and deriving high-level semantic features. We identify five major categories of the state-of-the-art techniques in narrowing down the 'semantic gap': (1) using object ontology to define high-level concepts; (2) using machine learning methods to associate low-level features with query concepts; (3) using relevance feedback to learn users' intention; (4) generating semantic template to support high-level image retrieval; (5) fusing the evidences from HTML text and the visual content of images for WWW image retrieval. In addition, some other related issues such as image test bed and retrieval performance evaluation are also discussed. Finally, based on existing technology and the demand from real-world applications, a few promising future research directions are suggested.
••01 Oct 2001
TL;DR: This work proposes the use of a support vector machine active learning algorithm for conducting effective relevance feedback for image retrieval and achieves significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
Abstract: Relevance feedback is often a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively determinines a user's desired output or query concept by asking the user whether certain proposed images are relevant or not. For a relevance feedback algorithm to be effective, it must grasp a user's query concept accurately and quickly, while also only asking the user to label a small number of images. We propose the use of a support vector machine active learning algorithm for conducting effective relevance feedback for image retrieval. The algorithm selects the most informative images to query a user and quickly learns a boundary that separates the images that satisfy the user's query concept from the rest of the dataset. Experimental results show that our algorithm achieves significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
TL;DR: This survey tries to clarify the different problem definitions related to subspace clustering in general; the specific difficulties encountered in this field of research; the varying assumptions, heuristics, and intuitions forming the basis of different approaches; and how several prominent solutions tackle different problems.
Abstract: As a prolific research area in data mining, subspace clustering and related problems induced a vast quantity of proposed solutions. However, many publications compare a new proposition—if at all—with one or two competitors, or even with a so-called “naive” ad hoc solution, but fail to clarify the exact problem definition. As a consequence, even if two solutions are thoroughly compared experimentally, it will often remain unclear whether both solutions tackle the same problem or, if they do, whether they agree in certain tacit assumptions and how such assumptions may influence the outcome of an algorithm. In this survey, we try to clarify: (i) the different problem definitions related to subspace clustering in general; (ii) the specific difficulties encountered in this field of research; (iii) the varying assumptions, heuristics, and intuitions forming the basis of different approaches; and (iv) how several prominent solutions tackle different problems.
01 Jan 2007
TL;DR: A survey of CBIR systems is provided and the fundamental properties and techniques used in these systems are explained, including text-based information retrieval and why it does not work for searching through collections of images.
Abstract: With today’s large increase in digital images and automatically generated imagery, such as videos and stills generated from surveillance equipment, the need for efficient image retrieval and indexing has become fundamental. Since text-based information retrieval has been shown to perform very poorly when searching through images, research has been active in the field of content-based image retrieval (CBIR). CBIR systems make use of the properties of images in order to compare them and extract content by matching the query image. Comparing features – such as color, texture, and shape – allows for better retrieval accuracy; however, the algorithms used are still very limited. This paper will provide a survey of CBIR systems and explain the fundamental properties and techniques used in these systems. First, the history of CBIR systems will be discussed together with some typical CBIR systems. After this, the paper will touch on text-based information retrieval and explain why it does not work for searching through collections of images. The latter portion of this document will provides an overview of a typical CBIR system and the main techniques involved in querying such a system. Finally, image features and indexing schemes will be described.
TL;DR: This work proposes a content-based soft annotation procedure for providing images with semantical labels, and experiments with two learning methods, support vector machines (SVMs) and Bayes point machines (BPMs), to select a base binary-classifier for CBSA.
Abstract: We propose a content-based soft annotation (CBSA) procedure for providing images with semantical labels. The annotation procedure starts with labeling a small set of training images, each with one single semantical label (e.g., forest, animal, or sky). An ensemble of binary classifiers is then trained for predicting label membership for images. The trained ensemble is applied to each individual image to give the image multiple soft labels, and each label is associated with a label membership factor. To select a base binary-classifier for CBSA, we experiment with two learning methods, support vector machines (SVMs) and Bayes point machines (BPMs), and compare their class-prediction accuracy. Our empirical study on a 116-category 25K-image set shows that the BPM-based ensemble provides better annotation quality than the SVM-based ensemble for supporting multimodal image retrievals.