Recent Advance in Content-based Image Retrieval: A Literature Survey

Abstract

The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content-based image retrieval (CBIR), which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content-based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research.

arXiv comment22 pages

References in corpus (8)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
• Faster and better: a machine learning approach to corner detection
• Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
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• Sketch-based 3D Shape Retrieval using Convolutional Neural Networks
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• Regularizing Class-wise Predictions via Self-knowledge Distillation
• Semi-parametric Image Inpainting
• Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning
• Machine learning based co-creative design framework
• Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-segmentation
• What and Where: A Context-based Recommendation System for Object Insertion
• Visual-Semantic Embedding Model Informed by Structured Knowledge
• Dynamic Spatial Verification for Large-Scale Object-Level Image Retrieval
• Multimodal Prediction based on Graph Representations
• Combating Ambiguity for Hash-code Learning in Medical Instance Retrieval
• PrivateMail: Supervised Manifold Learning of Deep Features With Differential Privacy for Image Retrieval