1265628
Image Categorization by Learning and Reasoning with Regions
Designing computer programs to automatically categorize images using low-level features is a challenging research topic in computer vision. In this paper, we present a new learning technique, which extends Multiple-Instance Learning (MIL), and its application to the problem of region-based image categorization. Images are viewed as bags, each of which contains a number of instances corresponding to regions obtained from image segmentation. The standard MIL problem assumes that a bag is labeled positive if at least one of its instances is positive; otherwise, the bag is negative. In the proposed MIL framework, DD-SVM, a bag label is determined by some number of instances satisfying various properties. DD-SVM first learns a collection of instance prototypes according to a Diverse Density (DD) function. Each instance prototype represents a class of instances that is more likely to appear in bags with the specific label than in the other bags. A nonlinear mapping is then defined using the instance prototypes and maps every bag to a point in a new feature space, named the bag feature space. Finally, standard support vector machines are trained in the bag feature space. We provide experimental results on an image categorization problem and a drug activity prediction problem.
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.
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.
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.
17964420
A learning process to the identification of feature points on Chinese characters
The paper describes a novel stroke extraction approach to identify the feature points of a character, using line-filtering and learning-based techniques. The line-filtering technique based on convolution operations with a set of one-dimensional (1D) Gabor templates efficiently extracts the stroke segments from noisy and degraded characters. Furthermore, the relationship between endpoints of stroke segments is modeled as junction structure during a learning process. Finally, each endpoint is identified as a feature point to determine the junction structure by the learning-based technique, rather than rule-based techniques with manual rule creation. Experimental results indicate that the learning-based technique can generalize learning knowledge to identify 1200 feature points with an average identification rate of 93.58% for test set, using k-fold cross-validation testing.
14507980
Effects of term segmentation on Chinese/English cross-language information retrieval
The majority of recent Cross-Language Information Retrieval (CLIR) research has focused on European languages. CLIR problems that involve East Asian languages such as Chinese introduce additional challenges, because written Chinese texts lack boundaries between terms. The paper examines three Chinese segmentation techniques in combination with two variants of dictionary-based Chinese to English query translation. The results indicate that failure to segment terms, particularly technical terms and names, can have a cascading effect that reduces retrieval effectiveness. Task-tuned segmentation algorithms and alternative term weighting strategies are suggested as productive directions for future work.
7207895
Space-code bloom filter for efficient traffic flow measurement
Per-flow traffic measurement is critical for usage accounting, traffic engineering, and anomaly detection. Previous methodologies are either based on random sampling (e.g., Cisco's NetFlow), which is inaccurate, or only account for the "elephants". Our paper introduces a novel technique for measuring per-flow traffic approximately, for all flows regardless of their sizes, at very high-speed (say, OC192+). The core of this technique is a novel data structure called Space Code Bloom Filter (SCBF). A SCBF is an approximate representation of a multiset; each element in this multiset is a traffic flow and its multiplicity is the number of packets in the flow. SCBF employs a Maximum Likelihood Estimation (MLE) method to measure the multiplicity of an element in the multiset. Through parameter tuning, SCBF allows for graceful tradeoff between measurement accuracy and computational and storage complexity. SCBF also contributes to the foundation of data streaming by introducing a new paradigm called blind streaming. We evaluated the performance of SCBF on packet traces gathered from a tier-1 ISP backbone and through mathematical analysis. Our preliminary results demonstrate that SCBF achieves reasonable mea-surement accuracy with very low storage and computational complexity.
8350188
Constraint Propagation And Progressive Verification For Component-Based Process Model
System assembly is one of the major issues in engineering complex component-based systems. This is especially true when heterogeneous, COTS and GOTS distributed systems, typical in industrial applications, are involved. The goal of system assembly is not only to make constituent components work together, but also to ensure that the components as a whole behave consistently and guarantee certain end-to-end properties. Despite recent advances, there is a lack of understanding about software composability, as well as theory and techniques for checking and verifying component-based systems. A theory of software system constraints about components, their environment and about system as a whole is the necessary foundation toward solid understanding of the composability of component-based systems. In this paper, we present a systematic approach for constraint specification and constraint propagation in concert with design refinement with a novel technique to ensure consistency between system-wide and component constraints in a design composition process of component-based systems. The consistent constraint propagation is used in our approach to drive progressive verification of the design. It allows us to verify overall design composition without interference of internal details of component designs. Verification is done separately at architectural and component levels without having to compose results of component analyses. A component can be safely replaced with alternative design without re-verifying the overall system composition so long as the replacement conforms to the corresponding interface and component constraint(s).
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.