Random interest regions for object recognition based on texture descriptors and bag of features

In this work we propose a novel method for object recognition based on a random selection of interest regions, texture features (local binary/ternary patterns and local phase quantization) for describing each region, a bag-of-features approach for describing each object, and classification using support vector machines (SVMs). In our approach, a set of features is extracted from each subwindow of the object image. These sets are quantified, and the resulting global descriptor vector is used as a characterization of the image (e.g., as a feature vector for learning an image classification rule based on a SVM classifier). The standard texture descriptor is not widely utilized in region description. One of the first texture descriptors explored in region description is the CS-LBP descriptor, where a local binary pattern (LBP) feature is used as the local feature in the SIFT method, the most well-known object recognition algorithm. Our approach based on texture descriptors is much simpler than the SIFT algorithm, yet it performs comparably well. Furthermore, we show that the fusion between our approach and SIFT obtains a very high AUC in the well-known PASCAL VOC2006 dataset.