Cross-media relevance mining for evaluating text-based image search engine

摘要

Targeted at MSR-Bing Image Retrieval grand challenge, we accumulate the experience from the one in 2013, and the make further investigation into different models to solve the relevance assessment problem. Generally speaking, the assessment of relevance between text query and image list is very hard due to the semantic gap. It's not easy to find the “mapping” from text query into the visual world. Solutions from 2013 MSR-Bing grand challenge are discussed in this paper. Combining with our own observation, we give some insights into why some solutions work well, while others not. Our main solution is to combine the deep learning features with the wining solution of last year (average similarity measurement and page rank), since the deep learning features have more compact representation than the traditional BoWs features, and deep learning features are efficient (on a descent GPU) with very good performance. Our solution achieved the 1st place in MSR-Bing grand challenge 2014. Finally, we give the running time of our solution in the testing phase for the 2014 ICME testing set and development set, respectively.