13962172
Diversity in multimedia information retrieval research
Multimedia information retrieval is a highly diverse field. A variety of data types, research problems,methodologies are involved.Researchers in the field come from very different disciplines, ranging from mathematical and physical sciences, computational sciences and engineering, to application domains. The panel, consisting of highly visible active researchers from both academia and the industry,opens a discussion on the importance of diversity to the healthy growth of the field. This paper records their opinions expressed at the panel.
735304
PARAgrab: a comprehensive architecture for web image management and multimodal querying
We demonstrate PARAgrab - a scalable Web image archival, retrieval, and annotation system that supports multiple querying modalities. The underlying architecture of our large-scale Web image database is described. Querying and visualization techniques used in the system are explained.
4227250
Content-based image retrieval by clustering
In a typical content-based image retrieval (CBIR) system, query results are a set of images sorted by feature similarities with respect to the query. However, images with high feature similarities to the query may be very different from the query in terms of semantics. This is known as the semantic gap. We introduce a novel image retrieval scheme, CLUster-based rEtrieval of images by unsupervised learning (CLUE), which tackles the semantic gap problem based on a hypothesis: semantically similar images tend to be clustered in some feature space. CLUE attempts to capture semantic concepts by learning the way that images of the same semantics are similar and retrieving image clusters instead of a set of ordered images. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. Therefore, the clusters give the algorithm as well as the users semantic relevant clues as to where to navigate. CLUE is a general approach that can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus it may be embedded in many current CBIR systems. Experimental results based on a database of about 60, 000 images from COREL demonstrate improved performance.
7844670
A sparse support vector machine approach to region-based image categorization
Automatic image categorization using low-level features is a challenging research topic in computer vision. In this paper, we formulate the image categorization problem as a multiple-instance learning (MIL) problem by viewing an image as a bag of instances, each corresponding to a region obtained from image segmentation. We propose a new solution to the resulting MIL problem. Unlike many existing MIL approaches that rely on the diverse density framework, our approach performs an effective feature mapping through a chosen metric distance function. Thus the MIL problem becomes solvable by a regular classification algorithm. Sparse SVM is adopted to dramatically reduce the regions that are needed to classify images. The selected regions by a sparse SVM approximate to the target concepts in the traditional diverse density framework. The proposed approach is a lot more efficient in computation and less sensitive to the class label uncertainty. Experimental results are included to demonstrate the effectiveness and robustness of the proposed method.
1791876
A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval
This paper proposes a fuzzy logic approach, UFM (unified feature matching), for region-based image retrieval. In our retrieval system, an image is represented by a set of segmented regions, each of which is characterized by a fuzzy feature (fuzzy set) reflecting color, texture, and shape properties. As a result, an image is associated with a family of fuzzy features corresponding to regions. Fuzzy features naturally characterize the gradual transition between regions (blurry boundaries) within an image and incorporate the segmentation-related uncertainties into the retrieval algorithm. The resemblance of two images is then defined as the overall similarity between two families of fuzzy features and quantified by a similarity measure, UFM measure, which integrates properties of all the regions in the images. Compared with similarity measures based on individual regions and on all regions with crisp-valued feature representations, the UFM measure greatly reduces the influence of inaccurate segmentation and provides a very intuitive quantification. The UFM has been implemented as a part of our experimental SIMPLIcity image retrieval system. The performance of the system is illustrated using examples from an image database of about 60,000 general-purpose images.
14843520
Deriving knowledge from figures for digital libraries
Figures in digital documents contain important information. Current digital libraries do not summarize and index information available within figures for document retrieval. We present our system on automatic categorization of figures and extraction of data from 2-D plots. A machine-learning based method is used to categorize figures into a set of predefined types based on image features. An automated algorithm is designed to extract data values from solid line curves in 2-D plots. The semantic type of figures and extracted data values from 2-D plots can be integrated with textual information within documents to provide more effective document retrieval services for digital library users. Experimental evaluation has demonstrated that our system can produce results suitable for real-world use.
17726002
Average-case completeness of a word problem for groups
This paper presents a bounded word problem for groups whose random instances are hard on average. The problem is to decide, when given a finitely presented group G, words ~, y, z, and an integer k, whether (Z–lyZ)Z can be obtained from Z(x–l yz) in G using elementary Tietze transformations for at most k times. We show that the problem is average-case NP-complete when instances are chosen at random under a uniform distribution. NP-completeness of more general bounded word problems for groups is shown as well.
414995
Combining bidirectional translation and synonymy for cross-language information retrieval
This paper introduces a general framework for the use of translation probabilities in cross-language information retrieval based on the notion that information retrieval fundamentally requires matching what the searcher means with what the author of a document meant. That perspective yields a computational formulation that provides a natural way of combining what have been known as query and document translation. Two well-recognized techniques are shown to be a special case of this model under restrictive assumptions. Cross-language search results are reported that are statistically indistinguishable from strong monolingual baselines for both French and Chinese documents.
21909317
Generating Concept Hierarchies from Text for Intelligence Analysis
It is important to automatically extract key information from sensitive text documents for intelligence analysis. Text documents are usually unstructured and information extraction from texts usually focuses on named entity extraction that is insufficient to reflect the concepts contained within documents. Other research on texts focuses on classifying the whole text into categories. However, texts might be diverse in subjects and suffer from classification accuracy. In this paper, our goal is to present a feasible approach to extract relevant information from texts and organize into concept hierarchies with the help of Web corpora. The approach is designed considering the need of information intelligence and the adaptation to different application domains.
11494425
An efficient VLSI switch-box router
A tool for switch-box routing that can route regions with cyclic constraints and with terminals on three or four sides is presented. A divide-and-conquer algorithm is used to explore the greedy channel routing idea, using techniques such as routing area partitioning, dynamic routing strategies, and sweeping concurrent bidirectional columns. The routing area is decomposed into three parts by two special lines cut parallel, making routing easier. The algorithm completely routes Burstein's switch-box problem and with an extension also routes the Deutsch channel example in 19 tracks.<<ETX>>
12807394
A novel data caching scheme for multimedia servers
Abstract In this paper, we propose a Bi-directional Fragmental Pipelining (BFP) technique and its variable buffer size data-caching scheme BFPV to reduce the disk I/O bandwidth requirement for multimedia servers. Our mathematical analysis shows that the BFP technique is superior to the traditional unidirectional pipelining technique in terms of memory buffer space requirement. We further demonstrate that the memory buffer management using BFPV is better than that of using the fixed buffer size approach BFPF. We have mathematically proved that BFPV saves more disk I/O bandwidth than BFPF does using the same memory buffer space. Our simulation results have quantitatively confirmed our analysis.