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Designing Category-Level Attributes for Discriminative Visual Recognition ∗
"... Attribute-based representation has shown great promises for visual recognition due to its intuitive interpretation and cross-category generalization property. However, human efforts are usually involved in the attribute designing process, making the representation costly to obtain. In this paper, we ..."
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Cited by 26 (1 self)
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, we propose a novel formulation to automatically design discriminative “category-level attributes”, which can be efficiently encoded by a compact category-attribute matrix. The formulation allows us to achieve intuitive and critical design criteria (category-separability, learnability) in a principled
Designing Category-Level Attributes for Discriminative Visual Recognition∗
"... Attribute-based representation has shown great promises for visual recognition due to its intuitive interpretation and cross-category generalization property. However, human efforts are usually involved in the attribute designing pro-cess, making the representation costly to obtain. In this paper, w ..."
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, we propose a novel formulation to automatically de-sign discriminative “category-level attributes”, which can be efficiently encoded by a compact category-attribute ma-trix. The formulation allows us to achieve intuitive and crit-ical design criteria (category-separability, learnability) in a
Designing Category-Level Attributes for Discriminative Visual Recognition∗
"... Attribute-based representation has shown great promis-es for visual recognition due to its intuitive interpretation and cross-category generalization property. However, hu-man efforts are usually involved in the attribute designing process, making the representation costly to obtain. In this paper, ..."
Abstract
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, we propose a novel formulation to automatically de-sign discriminative “category-level attributes”, which can be efficiently encoded by a compact category-attribute ma-trix. The formulation allows us to achieve intuitive and crit-ical design criteria (category-separability, learnability) in a
Additional remarks on designing category-level attributes for discriminative visual recognition
, 2013
"... This is the supplementary material for Designing Category-Level Attributes for Discriminative Visual Recognition [3]. We first provide an overview of the proposed approach in Section 1. The proof of the theorem is shown in Section 2. Additional remarks of the proposed attribute design algorithm are ..."
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Cited by 3 (1 self)
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This is the supplementary material for Designing Category-Level Attributes for Discriminative Visual Recognition [3]. We first provide an overview of the proposed approach in Section 1. The proof of the theorem is shown in Section 2. Additional remarks of the proposed attribute design algorithm
Additional Remarks on Designing Category-Level Attributes for Discriminative Visual Recognition∗
"... This is the supplementary material for Designing Category-Level Attributes for Dis-criminative Visual Recognition [3]. We first provide an overview of the proposed ap-proach in Section 1. The proof of the theorem is shown in Section 2. Additional remarks of the proposed attribute design algorithm ar ..."
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This is the supplementary material for Designing Category-Level Attributes for Dis-criminative Visual Recognition [3]. We first provide an overview of the proposed ap-proach in Section 1. The proof of the theorem is shown in Section 2. Additional remarks of the proposed attribute design algorithm
Face Recognition: A Literature Survey
, 2000
"... ... This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into ..."
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Cited by 1363 (21 self)
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into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition,
The PASCAL Visual Object Classes (VOC) challenge
, 2009
"... ... is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has be ..."
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Cited by 624 (20 self)
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... is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has
High confidence visual recognition of persons by a test of statistical independence
- IEEE Trans. on Pattern Analysis and Machine Intelligence
, 1993
"... Abstruct- A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person’s face is the detailed texture of each eye’s iris: An estimate of its statistical complexity in a samp ..."
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Cited by 596 (8 self)
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Abstruct- A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person’s face is the detailed texture of each eye’s iris: An estimate of its statistical complexity in a
Visual categorization with bags of keypoints
- In Workshop on Statistical Learning in Computer Vision, ECCV
, 2004
"... Abstract. We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors of im ..."
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Cited by 984 (14 self)
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Abstract. We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors
Shape Matching and Object Recognition Using Shape Contexts
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2001
"... We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by (1) solv- ing for correspondences between points on the two shapes, (2) using the correspondences to estimate an aligning transform ..."
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Cited by 1787 (21 self)
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We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by (1) solv- ing for correspondences between points on the two shapes, (2) using the correspondences to estimate an aligning
Results 1 - 10
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