Effective, Efficient, and Scalable Unsupervised Distance Learning in Image Retrieval Tasks
Lucas Pascotti Valem, Daniel Carlos Guimarães Pedronette, Ricardo da Silva Torres, Edson Borin, Jurandy Almeida · 2015
Various unsupervised learning methods have been proposed with significant improvements in the effectiveness of image search systems. However, despite the relevant effectiveness gains, these approaches commonly require high computation efforts, not addressing properly efficiency and scalability requirements. In this paper, we present a novel unsupervised learning approach for improving the effectiveness of image retrieval tasks. The proposed method is also scalable and efficient as it exploits parallel and heterogeneous computing on CPU and GPU devices. Extensive experiments were conducted considering five different public image collections and several descriptors. This rigorous experimental protocol evaluates the effectiveness, efficiency, and scalability of the proposed approach, and compares it with previous methods. Experimental results demonstrate that high effectiveness gains (up to +29%) can be obtained requiring small run times.