Democratic Diffusion Aggregation for Image Retrieval
Content-based image retrieval is an important research topic in the multimedia field. In large-scale image search using local features, image features are encoded and aggregated into a compact vector to avoid indexing each feature individually. In the aggregation step, sum-aggregation is wildly used in many existing works and demonstrates promising performance. However, it is based on a strong and implicit assumption that the local descriptors of an image are identically and independently distributed in descriptor space and image plane. To address this problem, we propose a new aggregation method named democratic diffusion aggregation (DDA) with weak spatial context embedded. The main idea of our aggregation method is to re-weight the embedded vectors before sum-aggregation by considering the relevance among local descriptors. Different from previous work, by conducting a diffusion process on the improved kernel matrix, we calculate the weighting coefficients more efficiently without any iterative optimization. Besides considering the relevance of local descriptors from different images, we also discuss an efficient query fusion strategy which uses the initial top-ranked image vectors to enhance the retrieval performance. Experimental results show that our aggregation method exhibits much higher efficiency (about × 14 faster) and better retrieval accuracy compared with previous methods, and the query fusion strategy consistently improves the retrieval quality.