ユーザフィードバックを利用した重み付けに基づく映像検索精度の向上

摘要

本文では，映像検索精度の向上を目的としたユーザフィードバックを利用した重み付けに基づくリランキング手法を提案する.提案手法ではまず，検索より得られるランキングリストからユーザが検索意図に即した映像を複数選択する.次に選択した映像に優先順位を付け，最も適した映像とその他の映像間の類似度に基づいた重みの算出により，最適な重みを得る.最後に重み付けを行ったユーザの選択映像と検索より得られた映像間の類似度に基づいて適合性を評価することでリランキングを行う.以上より，ユーザの検索意図に即した映像が上位に配置されたランキングリストの推定が可能となり，映像検索精度の向上を実現する.%Video reranking is an effective way for improving the retrieval performance of keyword-based video search engines. A fundamental issue underlying the success of existing video reranking approaches is the ability in identifying potentially useful recurrent patterns from the initial search results. These patterns can be leveraged to upgrade the ranks of visually similar videos, which are also likely to be relevant. However, mining useful patterns without understanding query may lead to incorrect judgment in reranking. We explore the user selected data, which can be viewed as the footprints of user searching behavior, as an effective means of understanding query, for providing the basis on identifying the recurrent patterns that are potentially helpful for reranking. In this paper, a new reranking algorithm, named user feedback assisted multi-modality reranking, is proposed. The algorithm leverages selected videos to locate similar videos that are not selected, and reranks them in a multi-modality learning scheme. Experimental results obtained by applying the proposed method to a real-world video collection show its effectiveness.