Recent works on Content Based Image Retrieval rely on Bag of Visual Words to index images. Analogically to the Bag of Words approach used in text retrieval, this model allows describing an image as a bag of elementary local features called visual words. As a result, an image is represented by a vector of weights, where each weight corresponds to the importance of a visual word in the image. The choice of local features and the weighting scheme are very important to perform image retrieval. The existing weighting schemes are mostly migrated from text retrieval domain and don’t take into account fundamental differences between textual words and visual words. In this paper, a novel approach based on Scale Invariant Features Transform (SIFT) features and a new weighting scheme is proposed. The proposed scheme uses a fuzzy representation to index images with a more robust signature. Experimental results with the Coil-100 image database demonstrate that the proposed method produces better performance than known term weighting representations.
['Wassim Bouachir', 'Mustapha Kardouchi', 'Nabil Belacel']
Improving Bag of Visual Words Image Retrieval: A Fuzzy Weighting Scheme for Efficient Indexation