Retrieving Objects by Partitioning

Retrieving objects from large image collection is challenging due to the so-called background-interference, i.e., matching between query object and reference images is usually confused by cluttered background, especially when objects are small. In this paper, we propose an object retrieval technique addressing this problem by partitioning the images. Specifically, several object proposals are partitioned from the images by jointly optimizing their objectness and coverage. The proposal set with maximum objectness score and minimum redundancy is obtained. Therefore, the interference of cluttered background is greatly reduced. Next, the objects are retrieved based on the partitioned proposals, separately and independently to the background. Our method is featured by the fine partitioning, which not only removes interferences from background, but also significantly reduces the number of objects to index. In this way, the effectiveness and efficiency are both achieved, which better suits big data retrieval. Subsequently, feature coding on partitioned objects generates much meaningful representation, and object level connectivity also introduces novel clues into the reranking. Extensive experiments on three popular object retrieval benchmark datasets (Oxford Buildings, Paris, Holiday) show the effectiveness of our method in retrieving small objects out of big data.