Relevance feedback for real-world human action retrieval

Content-based video retrieval is an increasingly popular research field, in large part due to the quickly growing catalogue of multimedia data to be found online. Even though a large portion of this data concerns humans, however, retrieval of human actions has received relatively little attention. Presented in this paper is a video retrieval system that can be used to perform a content-based query on a large database of videos very efficiently. Furthermore, it is shown that by using ABRS-SVM, a technique for incorporating Relevance feedback (RF) on the search results, it is possible to quickly achieve useful results even when dealing with very complex human action queries, such as in Hollywood movies.

► Realistic, noisy videos are searched using content-based information retrieval.
► ABRS-SVMs are incorporated for relevance feedback on the search results.
► Feedback shows strong improvement in accuracy of search results on real-world action datasets.