Online image search result grouping with MapReduce-based image clustering and graph construction for large-scale photos

• We propose an image search result grouping system for large scale image data.
• Image graphs and clusters are precomputed parallely for better system efficiency.
• Convergence checking of parallel affinity propagation clustering is proposed.
• We explore both semantic and visual features on constructing image graph.
• Comprehensive user study is conducted for comparing this system with two baselines.

Current image search system uses paged image list to show search results. However, the problems such as query ambiguity make users hard to find search targets in such image list. In this work, we propose an image search result grouping system that summarizes image search results in semantic and visual groups. We use MapReduce-based image graph construction and image clustering methods to deal with scalability problem on this system. By precomputing image graphs and image clusters at offline stage, this system can be efficient at responding user query. The experiments on two large scale Flickr image datasets are conducted for our system. Compared with using single machine, our graph construction method is 69 times faster. We conduct a comprehensive user study to compare our approach with state-of-the-art baseline methods. We find that our approach generates competent image groups with a 2–100 times speeded-up.