Kernel Codebooks for Scene Categorization

This paper introduces a method for scene categorization by modeling ambiguity in the popular codebook approach. The codebook approach describes an image as a bag of discrete visual codewords, where the frequency distributions of these words are used for image categorization. There are two drawbacks to the traditional codebook model: codeword uncertainty and codeword plausibility. Both of these drawbacks stem from the hard assignment of visual features to a single codeword. We show that allowing a degree of ambiguity in assigning codewords improves categorization performance for three state-of-the-art datasets.

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Jan van Gemert, Jan-Mark Geusebroek, Cor J. Veenman, Arnold W. M. Smeulders
• Keywords: Codeword Plausibility, Codeword Uncertainty, Computer Vision, ECCV 2008, Popular Codebook Approach, Single Codeword, Traditional Codebook Model