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...

• Type: Conference
• Year: 2007
• Where: ICRA
• Authors: Sungho Kim, In-So Kweon, Chil-Woo Lee
• Keywords: Codebook, ICRA 2007, Large Intra-class Variations, Robotics, Surface Marking