Collaborative Image Relevance Learning for Visual Re-Ranking
Jianbo Ouyang, Wengang Zhou, Min Wang, Qi Tian, Houqiang Li
Abstract
In content-based image retrieval, the initial retrieval result may be unsatisfactory, which can be refined with visual re-ranking techniques, such as query expansion, geometric verification, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . In this work, we approach visual re-ranking from a novel perspective. Observing that the contextual similarity of images from a retrieval result list exhibits strong visual relevance, we propose to collaboratively learn the semantic relevance among images for visual re-ranking. In our approach, we represent the image set of a fixed-length retrieval list into a correlation matrix, and learn the relevance of all image pairs simultaneously with a lightweight CNN model. To optimize the CNN model, a weighted MSE loss is defined, which takes into account the sparsity of labels. To find the optimal length of retrieval result list for different queries, we present a query sensitive selection method. We conduct comprehensive experiments on five benchmark datasets, and demonstrate the generality, and effectiveness of the proposed visual re-ranking method.