Contextual Noise Reduction for Domain Adaptive Near-Duplicate Retrieval on Merchandize Images

In this paper, we have proposed a novel method, which utilizes the contextual relationship among visual words for reducing the quantization errors in near-duplicate image retrieval (NDR). Instead of following the track of conventional NDR techniques, which usually search new solutions by borrowing ideas from the text domain, we propose to model the problem back to image domain, which results in a more natural way of solution search. The idea of the proposed method is to construct a context graph that encapsulates the contextual relationship within an image and treat the graph as a pseudo-image, so that classical image filters can be adopted to reduce the mis-mapped visual words which are contextually inconsistent with others. With these contextual noises reduced, the method provides purified inputs to the subsequent processes in NDR, and improves the overall accuracy. More importantly, the purification further increases the sparsity of the image feature vectors, which thus speeds up the conventional methods by 1662% times and makes NDR practical to online applications on merchandize images where the requirement of response time is critical. The way of considering contextual noise reduction in image domain also makes the problem open to all sophisticated filters. Our study shows the classic anisotropic diffusion filter can be employed to address the cross-domain issue, resulting in the superiority of the method to conventional ones in both effectiveness and efficiency.