Efficient Unsupervised Distance Learning through Rank Correlation Measures on Heterogeneous Systems

Daniel Carlos Pedronette, César Okada

Abstract

The huge growth of image collections have demanded methods capable of conducting effective and efficient image searches. Among the most promising approaches, the Content-Based Image Retrieval (CBIR) systems have established as an alternative for automatically taking into account the visual information. Despite the important results achieved, retrieving relevant images (effectiveness) in minimal time (efficiency) remains a challenge task. Recently, unsupervised learning algorithms have been proposed to improve the effectiveness of CBIR systems by exploiting similarity and ranking information. Such algorithms does not require any user information, but often demand high computational efforts. On the other hand, parallel and heterogeneous approaches constitute a feasible solution for high performance computing. In this paper, we discuss a parallel and accelerated solution for computing the RL-Sim∗ Algorithm, a recently proposed unsupervised image re-ranking approach. The proposed algorithm uses the OpenCL standard, exploiting both CPU and GPU devives in an Accelerated Processing Unit (APU). The experimental evaluation demonstrated that significant speedups were achieved when compared with the original approach.

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