Deep Learning for Content-Based Image Retrieval

TLDR

Learning effective feature representations and similarity measures is essential for CBIR, yet the semantic gap between low‑level pixels and high‑level concepts remains a major challenge that hampers real‑world systems. This study investigates whether deep learning can bridge the semantic gap in CBIR and quantifies the performance gains achievable with state‑of‑the‑art deep learning techniques. The authors evaluate a deep‑learning framework based on convolutional neural networks for CBIR, conducting extensive empirical experiments across diverse settings. The experiments yield encouraging results, highlighting significant improvements and offering insights for future research.

Abstract

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 examining a state-of-the-art deep learning method (Convolutional Neural Networks) for CBIR tasks under varied settings. From our empirical studies, we find some encouraging results and summarize some important insights for future research.