A discriminative spatial bag-of-word scheme with distinct patch

In the past few years, Bag of Word (BOW) plays an important role in the field of image retrieval and automatic annotation especially in large scale image databases. However, in practice, the spatial location information of visual word is often ignored. Despite much previous work, it remains a challenging task to extract the spatial location information of visual word. Recently, salient object detection has been attracting lots of interest and obtains a series of satisfactory results. In this paper, the novel approach can encode spatial location information into standard BOW representation which utilize salient object in the image. The proposed approach is so simple, yet useful and efficient enough to be applied in large-scale databases. The main properties of this approach are: (1) the potential strength of visual word´s spatial location information is extracted and a novel BOW scheme is proposed. (2) the advantage of standard BOW scheme is still retained such as rotational invariance, translational invariance and scaling invariance. (3) The new BOW scheme could significantly enhance the discriminative power. We evaluate our new framework on the standard dataset and the results have shown a significant improvement over the state-of-the-art methods.