Showing papers by "James Z. Wang published in 2002"

A region-based fuzzy feature matching approach to content-based image retrieval

Abstract: 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.

Learning-based linguistic indexing of pictures with 2--d MHMMs

Abstract: Automatic linguistic indexing of pictures is an important but highly challenging problem for researchers in computer vision and content-based image retrieval. In this paper, we introduce a statistical modeling approach to this problem. Categorized images are used to train a dictionary of hundreds of concepts automatically based on statistical modeling. Images of any given concept category are regarded as instances of a stochastic process that characterizes the category. To measure the extent of association between an image and the textual description of a category of images, the likelihood of the occurrence of the image based on the stochastic process derived from the category is computed. A high likelihood indicates a strong association. In our experimental implementation, the ALIP (Automatic Linguistic Indexing of Pictures) system, we focus on a particular group of stochastic processes for describing images, that is, the two-dimensional multiresolution hidden Markov models (2-D MHMMs). We implemented and tested the system on a photographic image database of 600 different semantic cat- egories, each with about 40 training images. Tested using 3,000 images outside the training database, the system has demonstrated good accuracy and high potential in linguistic indexing of these test images.

SST: an algorithm for finding near-exact sequence matches in time proportional to the logarithm of the database size.

Abstract: Motivation: Searches for near exact sequence matches are performed frequently in large-scale sequencing projects and in comparative genomics. The time and cost of performing these large-scale sequence-similarity searches is prohibitive using even the fastest of the extant algorithms. Faster algorithms are desired. Results: We have developed an algorithm, called SST (Sequence Search Tree), that searches a database of DNA sequences for near-exact matches, in time proportional to the logarithm of the database size n .I n SST, we partition each sequence into fragments of fixed length called ‘windows’ using multiple offsets. Each window is mapped into a vector of dimension 4 k which contains the frequency of occurrence of its component k-tuples, with k a parameter typically in the range 4‐6. Then we create a tree-structured index of the windows in vector space, with tree-structured vector quantization (TSVQ). We identify the nearest neighbors of a query sequence by partitioning the query into windows and searching the tree-structured index for nearest-neighbor windows in the database. When the tree is balanced this yields an O(log n) complexity for the search. This complexity was observed in our computations. SST is most effective for applications in which the target sequences show a high degree of similarity to the query sequence, such as assembling shotgun sequences or matching ESTs to genomic sequence. The algorithm is also an effective filtration method. Specifically, it can be used as a preprocessing step for other search methods to reduce the complexity of searching one large database against another. For the problem of identifying overlapping fragments in the assembly of 120 000 fragments from a 1.5 megabase genomic sequence, SST is 15 times faster than BLAST when we consider both building and searching the tree. For searching alone (i.e. after building

Large-scale emperor digital library and semantics-sensitive region-based retrieval

Abstract: In recent years, with the advent of fast-speed, broadband telecommunications networks, and information technology research for digital libraries, more and more distributed digital libraries with multimedia information have been developed everywhere in the world. While technologies are available, there is insufficient large-scale and coordinated digital content development. Furthermore, state-ofthe-art technologies developed in the research labs are rarely used to assist the content development and content analysis. This paper first discusses how the NSF/IDLP Project, Chinese Memory Net has capitalized on the rich multimedia resources in both analog and digital formats of an earlier cultural documentaries products, the awardwinning The First Emperor of China videodisc and multimedia CD, to further build large-scale digital contents of significant museum and historical/cultural/heritage materials for productive international collaboration among experts from interdisciplinary fields. The content has been used as a testbed for technology research in the area of semantics-sensitive region-based image retrieval. We demonstrate the use of the SIMPLIcity technology in browsing and retrieving of images.

Progressive display of very high resolution images using wavelets.

Abstract: Digital or digitized biomedical images often have very high resolutions', which make them difficult or impossible to display on computer screens. Therefore, it is desirable to develop a multiresolution display method with which users can freely browse the contents of those high resolution images. In this paper, we present an improved wavelet-based progressive image display algorithm by stressing on the encoding and decoding process. The encoder, which dynamically determines levels of transform and partition of coefficients, is based on a modified Haar wavelet transform. The decoder retrieves the necessary data and reconstructs the requested region at a scale specified by the user. A prototype system, which enables virtually any size of images to be displayed progressively, has been implemented based on this algorithm. The system has low computational complexity for both encoding and decoding process.