Boosting-based multiple kernel learning for image re-ranking

Re-ranking the returned images from a query relies on two important steps to improve its effectiveness: the estimation of the image relevance and the enhancement of the similarity function. However, attaining an effective visual similarity and an accurate re-ranking are quite challenging. We address these issues by first evaluating the image relevance to the query from the dataset according to the visual features and the co-occurrence of local patches of images. Then we boost the visual similarity measure associated with image relevance, and propose an enhancement algorithm, called Boosting-MKL, which not only incrementally learns the feature fusion but also generally preserves the initial local ranking. Specifically, we perform a random walk over a similarity graph for re-ranking. The experimental results demonstrate that our proposed approach significantly improves the effectiveness of visual similarity measure and the performance of image reranking.