An Efficient Approximate kNN Graph Method for Diffusion on Image Retrieval
This addresses efficiency issues in image retrieval for computer vision applications, though it is incremental as it builds on existing diffusion and LSH techniques.
The paper tackles the computational bottleneck of quadratic growth in kNN graph size for diffusion in image retrieval by proposing an LSH-based approximate method, achieving the same performance as exact kNN graphs but approximately 18 times faster on a dataset of 100,000 images.
The application of the diffusion in many computer vision and artificial intelligence projects has been shown to give excellent improvements in performance. One of the main bottlenecks of this technique is the quadratic growth of the kNN graph size due to the high-quantity of new connections between nodes in the graph, resulting in long computation times. Several strategies have been proposed to address this, but none are effective and efficient. Our novel technique, based on LSH projections, obtains the same performance as the exact kNN graph after diffusion, but in less time (approximately 18 times faster on a dataset of a hundred thousand images). The proposed method was validated and compared with other state-of-the-art on several public image datasets, including Oxford5k, Paris6k, and Oxford105k.