Learning query-class dependent weights in automatic video retrieval

Combining retrieval results from multiple modalities plays a crucial role for video retrieval systems, especially for automatic video retrieval systems without any user feedback and query expansion. However, most of current systems only utilize query independent combination or rely on explicit user weighting. In this work, we propose using query-class dependent weights within a hierarchial mixture-of-expert framework to combine multiple retrieval results. We first classify each user query into one of the four predefined categories and then aggregate the retrieval results with query-class associated weights, which can be learned from the development data efficiently and generalized to the unseen queries easily. Our experimental results demonstrate that the performance with query-class dependent weights can considerably surpass that with the query independent weights.