Rocchio-Based Relevance Feedback in Video Event Retrieval

This paper investigates methods for user and pseudo relevance feedback in video event retrieval. Existing feedback methods achieve strong performance but adjust the ranking based on few individual examples. We propose a relevance feedback algorithm (ARF) derived from the Rocchio method, which is a theoretically founded algorithm in textual retrieval. ARF updates the weights in the ranking function based on the centroids of the relevant and non-relevant examples. Additionally, relevance feedback algorithms are often only evaluated by a single feedback mode (user feedback or pseudo feedback). Hence, a minor contribution of this paper is to evaluate feedback algorithms using a larger number of feedback modes. Our experiments use TRECVID Multimedia Event Detection collections. We show that ARF performs significantly better in terms of Mean Average Precision, robustness, subjective user evaluation, and run time compared to the state-of-the-art.