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Deep Learning for Content-Based Image Retrieval: A Comprehensive Study by Ji Wan; Dayong Wang; Steven Chu Hong Hoi; Pengcheng Wu; Jianke Zhu; Yongdong Zhang; Jintao Li is a scholarly article available to read on EtoBox.

What is Deep Learning for Content-Based Image Retrieval: A Comprehensive Study about?

Learning effective feature representations and similarity measures are crucial to the retrieval performance of a content-based image retrieval (CBIR) system. Despite extensive research efforts for decades, it remains one of the most challenging open problems that considerably hinders the successes of real-world CBIR systems. The key challenge has been attributed to the well-known "semantic gap" issue that exists between low-level image pixels captured by machines and high-level semantic concepts perceived by human. Among various techniques, machine learning has been actively investigated as a possible direction to bridge the semantic gap in the long term. Inspired by recent successes of deep learning techniques for computer vision and other applications, in this paper, we attempt to address an open problem: if deep learning is a hope for bridging the semantic gap in CBIR and how much improvements in CBIR tasks can be achieved by exploring the state-of-the-art deep learning techniques for learning feature representations and similarity measures. Specifically, we investigate a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by exam

Author

Ji Wan; Dayong Wang; Steven Chu Hong Hoi; Pengcheng Wu; Jianke Zhu; Yongdong Zhang; Jintao Li

Publisher

ACM

Published

2014

Language

EN