Abstract
Nowadays the bag-of-visual-words is a very popular approach to perform the task of Visual Object Classification (VOC). Two key phases of VOC are the vocabulary building step, i.e. the construction of a `visual dictionary' including common codewords in the image corpus, and the assignment step, i.e. the encoding of the images by means of these codewords. Hard assignment of image descriptors to visual codewords is commonly used in both steps. However, as only a single visual word is assigned to a given feature descriptor, hard assignment may hamper the characterization of an image in terms of the distribution of visual words, which may lead to poor classification of the images. Conversely, soft assignment can improve classification results, by taking into account the relevance of the feature descriptor to more than one visual word. Fuzzy Set Theory (FST) is a natural way to accomplish soft assignment. In particular, fuzzy clustering can be well applied within the VOC framework. In this paper we investigate the effects of using the well-known Fuzzy C-means algorithm and its kernelized version to create the visual vocabulary and to perform image encoding. Preliminary results on the Pascal VOC data set show that fuzzy clustering can improve the encoding step of VOC. In particular, the use of KFCM provides better classification results than standard FCM and K-means.