Monday, July 16, 2012

Content-Based Analysis Improves Audiovisual Archive Retrieval

Original Article:

The paper “Content-Based Analysis Improves Audiovisual Archive Retrieval” by Bouke Huurnink, Cees Snoek, Maarten de Rijke, and Arnold Smeulders, which appears in the August issue of IEEE Transactions on Multimedia, is now available. Content-based video retrieval is maturing to the point where it can be used in real-world retrieval practices. One such practice is the audiovisual archive, whose users increasingly require fine-grained access to broadcast television content. In this paper, we take into account the information needs and retrieval data already present in the audiovisual archive, and demonstrate that retrieval performance can be significantly improved when content-based methods are applied to search. To the best of our knowledge, this is the first time that the practice of an audiovisual archive has been taken into account for quantitative retrieval evaluation. To arrive at our main result, we propose an evaluation methodology tailored to the specific needs and circumstances of the audiovisual archive, which are typically missed by existing evaluation initiatives. We utilize logged searches, content purchases, session information, and simulators to create realistic query sets and relevance judgments. To reflect the retrieval practice of both the archive and the video retrieval community as closely as possible, our experiments with three video search engines incorporate archive-created catalog entries as well as state-of-the-art multimedia content analysis results. A detailed query-level analysis indicates that individual content-based retrieval methods such as transcript-based retrieval and concept-based retrieval yield approximately equal performance gains. When combined, we find that content-based video retrieval incorporated into the archive’s practice results in significant performance increases for shot retrieval and for retrieving entire television programs. The time has come for audiovisual archives to start accommodating content-based video retrieval methods into their daily practice.

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