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Deep image retrieval: a survey
Wei Chen,Yu Liu,Weiping Wang,Erwin M. Bakker,Theodoros Georgiou,Paul Fieguth,Li Liu,Michael S. Lew +7 more
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In this article, the authors organize and review recent content-based image retrieval (CBIR) works that are developed based on deep learning algorithms and techniques, including insights and techniques from recent papers.Abstract:
In recent years a vast amount of visual content has been generated and shared from various fields, such as social media platforms, medical images, and robotics. This abundance of content creation and sharing has introduced new challenges. In particular, searching databases for similar content, i.e.content based image retrieval (CBIR), is a long-established research area, and more efficient and accurate methods are needed for real time retrieval. Artificial intelligence has made progress in CBIR and has significantly facilitated the process of intelligent search. In this survey we organize and review recent CBIR works that are developed based on deep learning algorithms and techniques, including insights and techniques from recent papers. We identify and present the commonly-used benchmarks and evaluation methods used in the field. We collect common challenges and propose promising future directions. More specifically, we focus on image retrieval with deep learning and organize the state of the art methods according to the types of deep network structure, deep features, feature enhancement methods, and network fine-tuning strategies. Our survey considers a wide variety of recent methods, aiming to promote a global view of the field of instance-based CBIR.read more
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DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features
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Learning Efficient Hash Codes for Fast Graph-Based Data Similarity Retrieval
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Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval.
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Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).