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50,468
Object class recognition by unsupervised scale-invariant learning
- In CVPR
, 2003
"... We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for all aspects of the object: shape, appearance, occlusion and ..."
Abstract
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Cited by 1127 (50 self)
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We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for all aspects of the object: shape, appearance, occlusion
Fast Object Class Recognition
, 2005
"... We propose a novel approach for object class recognition using scale invariant features and Gaussian Processes as our kernel-based classifier. We measure the performance of this approach in two stages: predicting the presence of a class of objects in images and localizing them. Our object class reco ..."
Abstract
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We propose a novel approach for object class recognition using scale invariant features and Gaussian Processes as our kernel-based classifier. We measure the performance of this approach in two stages: predicting the presence of a class of objects in images and localizing them. Our object class
Local features for object class recognition
- In Proceedings of the 10th IEEE International Conference on Computer Vision
, 2005
"... In this paper we compare the performance of local detectors and descriptors in the context of object class recognition. Recently, many detectors / descriptors have been evaluated in the context of matching as well as invariance to viewpoint changes [20]. However, it is unclear if these results can b ..."
Abstract
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Cited by 70 (6 self)
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In this paper we compare the performance of local detectors and descriptors in the context of object class recognition. Recently, many detectors / descriptors have been evaluated in the context of matching as well as invariance to viewpoint changes [20]. However, it is unclear if these results can
Unsupervised Visual Object Class Recognition
"... Object class recognition is a very well studied problem in the domain of Computer Vision. Lately, the most successful approaches extract informative descriptors from the images, organize them and use machine learning techniques for learning and classification tasks. A common approach involves the co ..."
Abstract
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Cited by 2 (0 self)
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Object class recognition is a very well studied problem in the domain of Computer Vision. Lately, the most successful approaches extract informative descriptors from the images, organize them and use machine learning techniques for learning and classification tasks. A common approach involves
Selection of scale-invariant parts for object class recognition
- In ICCV
, 2003
"... This paper introduces a novel method for constructing and selecting scale-invariant object parts. Scale-invariant local descriptors are first grouped into basic parts. A classifier is then learned for each of these parts, and feature selection is used to determine the most discriminative ones. This ..."
Abstract
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Cited by 158 (16 self)
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. Different local regions, classifiers and feature selection methods are quantitatively compared. Our evaluation shows that local invariant descriptors are an appropriate representation for object classes such as cars, and it underlines the importance of feature selection. marked in black in the figure
Object Class Recognition using SIFT and Bayesian Networks
"... Abstract. Several methods have been presented in the literature that successfully used SIFT features for object identification, as they are reasonably invariant to translation, rotation, scale, illumination and partial occlusion. However, they have poor performance for classification tasks. In this ..."
Abstract
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. In this work, SIFT features are used to solve problems of object class recognition in images using a two-step process. In its first step, the proposed method performs clustering on the extracted features in order to characterize the appearance of classes. Then, in the classification step, it uses a three layer
Feature Fusion in Improving Object Class Recognition
"... Abstract: Problem statement: Extraction of features in object class recognition researches previously gives attention to local features as discriminative features. This is because local features have invariant properties that are robust to viewpoints, translation and rotation. However this feature ..."
Abstract
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Abstract: Problem statement: Extraction of features in object class recognition researches previously gives attention to local features as discriminative features. This is because local features have invariant properties that are robust to viewpoints, translation and rotation. However this feature
Efficient object-class recognition by boosting contextual information
- In IbPRIA
, 2005
"... Abstract. Object-class recognition is one of the most challenging fields of pattern recognition and computer vision. Currently, most authors represent an object as a collection of parts and their mutual spatial relations. Therefore, two types of information are extracted: local information describin ..."
Abstract
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Cited by 4 (0 self)
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Abstract. Object-class recognition is one of the most challenging fields of pattern recognition and computer vision. Currently, most authors represent an object as a collection of parts and their mutual spatial relations. Therefore, two types of information are extracted: local information
Object class recognition using discriminative local features
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2005
"... apport de r e c herche ..."
Efficient clustering and matching for object class recognition
- In Proc. BMVC
, 2006
"... In this paper we address the problem of building object class representations based on local features and fast matching in a large database. We propose an efficient algorithm for hierarchical agglomerative clustering. We examine different agglomerative and partitional clustering strategies and compa ..."
Abstract
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Cited by 37 (4 self)
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. These improvements allow to deal with large sets of training data typically used in recognition of multiple object classes. 1
Results 1 - 10
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50,468
