Sketch-Based Image Retrieval with Multi-Clustering Re-Ranking

To improve the performance of sketch-based image retrieval (SBIR) methods, most existing SBIR methods develop brand new SBIR methods. In fact, a re-ranking approach, which can refine the retrieval results of SBIR methods, is also beneficial. Inspired by this, in this paper, an SBIR re-ranking approach based on multi-clustering is proposed. In order to make the re-ranking approach invisible to users and adaptive to different types of image datasets, we made it an unsupervised method using blind feedback. Distinguished from the existing methods, this re-ranking approach uses the semantic information of three types of images: edge maps, object images (images with black background and natural images' foreground objects) and natural images themselves. With the initial retrieval results of an SBIR method, our approach first does the clustering operation for three types of images. Then, we utilize the clustering results to generate a cluster score for each initial retrieval result. Finally, the cluster score is used to calculate the final retrieval scores for the initial retrieval results. The experiments on different SBIR datasets are conducted. Experimental results demonstrate that, by implementing our re-ranking approach, the retrieval accuracy of a variety of SBIR methods is increased. Furthermore, the comparisons between our re-ranking method and the existing re-ranking methods are given.