Video Search Reranking using Multimodel fusion Technique
Ravi Regulagadda, G. Yedukondalu · 2014
Abstract- Analyzing click-through data from a huge search engine log information shows that users are usually interested in the top-ranked portion of returned search results. So, it is crucial for search engines to achieve high accuracy on the top-ranked documents. While many methods exist for enhancing video search performance, they either pay less attention to the above factor or encounter difficulties in practical applications. In this thesis, we present an easy and quality reranking method, called CR-Reranking, to increase the retrieval effectiveness. To offer high accuracy on the top-ranked results, CR-Reranking employs a cross-reference(CR) strategy to fuse multimodal cues. Specifically, multimodal features are first utilized separately to rerank the initial returned results at the cluster level, and then all the ranked clusters from different modalities are cooperatively used to infer the shots with high relevance. After the fusion process the results shows that the search quality, especially on the top-ranked results, is improved significantly.