A New Strategy of the Bag-of-Word Method with a Multi-scale Representation for Evaluating Color Pairwise Descriptors

In the field of image categorization, the Bag-Of-Word has proved to be successful. It treats local image features as visual words. After collecting all local features, each image is represented by a histogram of occurrences of visual words. In this work, we propose an extension to the Bag-Of-Words (BOW) by integrating the spatial relationships information between local features. In a first step, we extract local features by using both multi-scale representation and color descriptors based on HSV-SIFT, opponent-SIFT, RGB-SIFT, rg-SIFT and transformed-color-SIFT. In a second step, and in order to represent the relationships between local features, we form pairwise color descriptors by joining pairs of spatially neighbor SIFT color features. In a third step, we encode the histograms which involve the occurrence of pairwise color descriptors by applying the BOW strategy and the Spatial Pyramid Representation (SPR). Finally, image classification is carried out by using Support Vector Machine (SVM) on the generated histograms. Our proposed method is tested and validated using the standard image datasets “Pascal Voc 2007”.