Cross Indexing With Grouplets

Shiliang Zhang, Xiaoyu Wang, Yuanqing Lin, Qi Tian

Abstract: Most of the current image indexing systems for retrieval view a database as a set of individual images. It limits the flexibility of the retrieval framework to conduct sophisticated cross-image analysis, resulting in higher memory consumption and sub-optimal retrieval accuracy. To conquer this issue, we propose cross indexing with grouplets, where the core idea is to view the database images as a set of grouplets, each of which is defined as a group of highly relevant images. Because a grouplet groups similar images together, the number of grouplets is smaller than the number of images, thus naturally leading to less memory cost. Moreover, the definition of a grouplet could be based on customized relations, allowing for seamless integration of advanced image features and data mining techniques like the deep convolutional neural network (DCNN) in off-line indexing . To validate the proposed framework, we construct three different types of grouplets , which are respectively based on local similarity , regional relation, and global semantic modeling. Extensive experiments on public benchmark datasets demonstrate the efficiency and superior performance of our approach.