Abstract. 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.
Jan et al. REF introduced an uncertainty method of modeling ambiguity in popular Bag-of-Visual-Words approach for scene categorization.
Kernel codebooks for scene categorization
ECCV 2008, PART III. LNCS
Computer Science