54136036
Region-based retrieval of biomedical images
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.
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.
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.
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.
5570863
Solving the small sample size problem in face recognition using generalized discriminant analysis
The goal of face recognition is to distinguish persons via their facial images. Each person's images form a cluster, and a new image is recognized by assigning it to the correct cluster. Since the images are very high-dimensional, it is necessary to reduce their dimension. Linear discriminant analysis (LDA) has been shown to be effective at dimension reduction while preserving the cluster structure of the data. It is classically defined as an optimization problem involving covariance matrices that represent the scatter within and between clusters. The requirement that one of these matrices be nonsingular restricts its application to datasets in which the dimension of the data does not exceed the sample size. For face recognition, however, the dimension typically exceeds the number of images in the database, resulting in what is referred to as the small sample size problem. Recently, the applicability of LDA has been extended by using the generalized singular value decomposition (GSVD) to circumvent the nonsingularity requirement, thus making LDA directly applicable to face recognition data. Our experiments confirm that LDA/GSVD solves the small sample size problem very effectively as compared with other current methods.
37176365
A content-based scheme for CT lung image retrieval
A content based scheme to retrieve computed tomographic images (CT) of the lung is presented. It consists of a visual based user interface to allow the query be made by line drawing the interested (abnormal) regions; and a training scheme to classify the relationship between the images stored in database. The system will output a set of candidate images that are texturally similar to the query image. We marked the abnormal portions of each training image by a polygonal or rectangular shape manually because it requires expert knowledge. Then, the texture features of each marked region are extracted on the DCT or SADCT transform domain. In the training stage, the extracted DCT/SADCT coefficients are fed into a Kohonen self organizing network to find the relationship for classification. In the query stage, the system first checks which texture category the query image is in, then uses some geometrical characteristics to identify the most likely candidate image. Experimental results show that 96% of the queries can be correctly retrieved where the original image is in the candidate set.
13282723
A cascading MRVQ-DCT scheme for the compression of sequence images
In this correspondence, a mean residual vector quantizer (MRVQ) is superimposed on the motion estimator for improving the intraframe coding quality of MPEG. The proposed scheme performs an MRVQ before executing the discrete cosine transformation (DCT) for the purpose of curing the DCT intrinsic trouble of blocking effect at low bit rate.
41575427
Ranking Web News Via Homepage Visual Layout and Cross-Site Voting
Reading news is one of the most popular activities when people surf the internet. As too many news sources provide independent news information and each has its own preference, detecting unbiased important news might be very useful for users to keep up to date with what are happening in the world. In this paper we present a novel method to identify important news in web environment which consists of diversified online news sites. We observe that a piece of important news generally occupies visually significant place in some homepage of a news site and import news event will be reported by many news sites. To explore these two properties, we model the relationship between homepages, news and latent events by a tripartite graph, and present an algorithm to identify important news in this model. Based on this algorithm, we implement a system TOPSTORY to dynamically generate homepages for users to browse important news reports. Our experimental study indicates the effectiveness of proposed approach.
54104798
Data allocation algorithms for distributed video servers