image near-duplicate retrieval using local dependencies in spatial-scale space
This paper presents an efficient and effective solution for retrieving Image Near-Duplicate (IND). Different from traditional methods, we analyze the local dependencies among region descriptors in a spatial-scale space. Such local dependencies in spatial-scale space(LDSS) encodes not only visual appearance but also the spatial and scale co-occurrence of them. The local dependencies are integrated over all spatial locations and multiple scales to form the image representation, which is invariant to spatial transformation and scale change. We evaluate our proposed LDSS method for IND retrieval using an existing benchmark as well as a new dataset extracted from the keyframes of TRECVID corpus. Compared to the state-of-the-art results, local dependencies in spatial-scale space(LDSS) approach has been shown to significantly improve the accuracy of IND retrieval.
image near-duplicate local dependencies co-occurrence bag-of-words