Weighted transformable spatial pyramid and scalable query for object retrieval

Object retrieval in the large-scale image corpus is an appealing, yet challenging task. Most of existing frameworks are based on bag-of-visual-words (BoVW) model. However, BoVW has an obvious drawback, i.e. lack of spatial information. In this paper, we propose weighted transformable spatial pyramid and scalable query for object retrieval. We first break the whole image into sub-images and then make these sub-images up in a new order in the final representation. Our method has two contributions: 1) relative spatial relationships of local features instead of absolute geometric layouts of features are encoded so that translation invariance is guaranteed, 2) scaling invariance in the image representation is ensured by scalable query. The experimental results show that our approach outperforms BoVW and traditional spatial pyramid matching.