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ICRA
2007
IEEE

Visual Categorization Robust to Large Intra-Class Variations using Entropy-guided Codebook

13 years 10 months ago
Visual Categorization Robust to Large Intra-Class Variations using Entropy-guided Codebook
Abstract— Categorizing visual elements is fundamentally important for autonomous mobile robots to get intelligence such as new object acquisition and topological place classification. The main problem of visual categorization is how to reduce the large intra-class variations, especially surface markings of man-made objects. In this paper, we present a robust method by introducing intermediate blurring and entropyguided codebook selection in a bag-of-words framework. Intermediate blurring can filter out the high frequency of surface markings and provide dominant shape information. Entropy of a hypothesized codebook can provide the necessary measure for the semantic parts among training exemplars. From the first step, a generative optimal codebook for each category is learned using the MDL (minimum description length) principle guided by entropy information. From the second step, a final set of codebook is learned using the discriminative method guided by the inter-category entropy...
Sungho Kim, In-So Kweon, Chil-Woo Lee
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ICRA
Authors Sungho Kim, In-So Kweon, Chil-Woo Lee
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