Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Recognition by Linear Combinations of Models
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Elements of information theory
Elements of information theory
Visual learning and recognition of 3-D objects from appearance
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The nature of statistical learning theory
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Joint Induction of Shape Features and Tree Classifiers
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Ordinal Measures for Image Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition by elastic bunch graph matching
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ACCV '95 Invited Session Papers from the Second Asian Conference on Computer Vision: Recent Developments in Computer Vision
Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Cubist Approach to Object Recognition
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Journal of Cognitive Neuroscience
Class-Specific, Top-Down Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
The equivalence of two-dimensional PCA to line-based PCA
Pattern Recognition Letters
Shape Representation and Classification Using the Poisson Equation
IEEE Transactions on Pattern Analysis and Machine Intelligence
SPIRE '09 Proceedings of the 16th International Symposium on String Processing and Information Retrieval
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ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
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SPIRE'10 Proceedings of the 17th international conference on String processing and information retrieval
Weakly supervised learning of part-based spatial models for visual object recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Redundant bit vectors for quickly searching high-dimensional regions
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
Journal of Discrete Algorithms
Developing social networks for artificial societies from survey data
SBP'10 Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and Prediction
Probabilistic learning of similarity measures for tensor PCA
Pattern Recognition Letters
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The task of visual classification is thce recognition of an object in the image as belonging to a general class of similar objects, such as a face, a car, a dog, and the like. This is a fundamental and natural task for biological visual systems, but it has proven difficult to perform visual classification by artificial computer vision systems. The main reason for this difficulty is the variability of shape within a class: different objects vary widely in appearance, and it is difficult to capture the essential shape features that characterize the members of one category and distinguish them from another, such as dogs from cats. In this paper we describe an approach to classification using a fragment-based representation. In this approach, objects within a class are represented in terms of common image fragments that are used as building blocks for representing a large variety of different objects that belong to a common class. The fragments are selected from a training set of images based on a criterion of maximizing the mutual information of the fragments and the class they represent. For the purpose of classification the fragments are also organized into types, where each type is a collection of alternative fragments, such as different hairline or eye regions for face classification. During classification, the algorithm detects fragments of the different types, and then combines the evidence for the detected fragments to reach a final decision. Experiments indicate that it is possible to trade off the complexity of fragments with the complexity of the combination and decision stage, and this tradeoff is discussed. The method is different from previous part-based methods in using class-specific object fragments of varying complexity, the method of selecting fragments, and the organization into fragment types. Experimental results of detecting face and car views show that the fragment-based approach can generalize well to a variety of novel image views within a class while maintaining low mis-classification error rates. We briefly discuss relationships between the proposed method and properties of parts of the primate visual system involved in object perception