optimizing visual search with implicit user feedback in interactive video retrieval
This paper describes an approach to optimize query by visual example results, by combining visual features and implicit user feedback in interactive video retrieval. To this end, we propose a framework, in which video processing is performed by employing well established techniques, while implicit user feedback analysis is realized with a graph based approach that processes the user actions and navigation patterns during a search session, in order to initiate semantic relations between the video segments. To combine the visual and implicit feedback information, we train a support vector machine classifier with positive and negative examples generated from the graph structured past user interaction data. Then, the classifier reranks the results of visual search that were initially based on visual features. This framework is embedded in an interactive video search engine and evaluated by conducting a user experiment in two phases: first, we record the user actions during typical retrieval sessions and then, we evaluate the reranking of the results of visual query by example. The evaluation and the results demonstrate that the proposed approach provides an improved ranking in most of the evaluated queries.
interactive video retrieval implicit feedback search engine machine learning