Fuzzy indexing for Bag of Features scene categorization

This paper presents a novel Bag of Features (BoF) method for image classification. The BoF approach describes an image as a set of local descriptors using a histogram, where each bin represents the importance of a visual word. This indexing approach has been frequently used for image classification, and we have seen several implementations, but crucial representation choices - such as the weighting schemes - have not been thoroughly studied in the literature. In our work, we propose a Fuzzy model as an alternative to known weighting schemes in order to create more representative image signatures. Furthermore, we use the Fuzzy signatures to train the Gaussian Naïve Bayesian Network and classify images. Experiments with Corel-1000 dataset demonstrate that our method outperforms the known implementations.