High-Dimensional Indexing for Video Retrieval

The video retrieval task raises many fundamental questions in computer vision and information retrieval, such as how to represent video items, what information can be directly extracted from them, and how to explore such information in order to satisfy the user’s information need. Video items are intrinsically complex, and the analysis of their content requires heavy computing processes. Excepting the case of text, multimedia content analysis does not result in the high-level concepts required by the generality of the search tasks. This brings about the so-called “semantic gap”, clearly identified as the main issue in multimedia retrieval Gudivada & Raghavan (1995); Smith (2007). Text communication is based on concepts, expressed in the user’s language and close to the way humans think. Searching text items may require a number of processing techniques like pattern matching, stemming, finding synonyms, translating, natural language analysis. Supposing that the user expresses his information need through words, the set of retrieved documents includes those containing precise or imprecise word matches and can be continuously enlarged to more and more semantically related documents. In the case of a video repository, however, there is not such a clear semantic channel between the object’s content and the user information need. The automatic video analysis may produce many descriptors related to the contents, the so-called low-level descriptors Bober (Jun 2001); Manjunath et al. (2001); Mufit Ferman et al. (2000), but hardly produces accurate descriptions close enough to human concepts Snoek & Smeulders (2010); Tesic & Smith (2006). This is a problem for a wide range of video-based applications, other than search and retrieval, such as human-computer interface, security and surveillance, copyright protection, and personal entertainment.