Content-Based Copy Retrieval Using Distortion-Based Probabilistic Similarity Search

Content-based copy retrieval (CBCR) aims at retrieving in a database all the modified versions or the previous versions of a given candidate object. In this paper, we present a copy-retrieval scheme based on local features that can deal with very large databases both in terms of quality and speed. We first propose a new approximate similarity search technique in which the probabilistic selection of the feature space regions is not based on the distribution in the database but on the distribution of the features distortion. Since our CBCR framework is based on local features, the approximation can be strong and reduce drastically the amount of data to explore. Furthermore, we show how the discrimination of the global retrieval can be enhanced during its post-processing step, by considering only the geometrically consistent matches. This framework is applied to robust video copy retrieval and extensive experiments are presented to study the interactions between the approximate search and the retrieval efficiency. Largest used database contains more than 1 billion local features corresponding to 30000 h of video