Saliency-driven image classification method based on histogram mining and image score

Since most image classification tasks involve discriminative information (i.e., saliency), this paper proposes a new bag-of-phrase (BoP) approach to incorporate this information. Specifically, saliency map and local features are first extracted from edge-based dense descriptors. These features are represented by histogram and mined with discriminative learning technique. Image score calculated from the saliency map is also investigated to optimize a support vector machine (SVM) classifier. Both feature map and kernel trick methods are explored to enhance the accuracy of the SVM classifier. In addition, novel inter- and intra-class histogram normalization methods are investigated to further boost the performance of the proposed method. Experiments using several publicly available benchmark datasets demonstrate that the proposed method achieves promising classification accuracy and superior performance over state-of-the-art methods.