【专知荟萃15】图像检索Image Retrieval知识资料全集（入门/进阶/综述/视频/代码/专家，附PDF下载）- 专知

【专知荟萃15】图像检索Image Retrieval知识资料全集（入门/进阶/综述/视频/代码/专家，附PDF下载）

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图像检索（Image Retrieval）专知荟萃

入门学习
进阶文章
综述
Tutorial
视频教程
代码
领域专家
Datasets

入门学习

相似图片搜索的原理阮一峰

[http://www.ruanyifeng.com/blog/2011/07/principle_of_similar_image_search.html\]

Google 图片搜索的原理是什么？

[https://www.zhihu.com/question/19726630]

基于内容的图像检索技（CBIR）术相术介绍

[http://blog.csdn.net/kezunhai/article/details/11614989]

图像检索：基于内容的图像检索技术

[http://yongyuan.name/blog/cbir-technique-summary.html]

基于内容的图像检索技术

[http://www.cs.cmu.edu/~juny/Prof/papers/Part2-CBIR.pdf\]

图像检索：CNN卷积神经网络与实战 CNN for Image Retrieval

[http://yongyuan.name/blog/CBIR-CNN-and-practice.html]

用Python和OpenCV创建一个图片搜索引擎的完整指南

[http://blog.csdn.net/kezunhai/article/details/46417041]

进阶文章

2011

Using Very Deep Autoencoders for Content-Based Image Retrieval

[https://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf\]

2013

Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data

[http://arxiv.org/abs/1312.4740]

2014

Neural Codes for Image Retrieval

[http://arxiv.org/abs/1404.1777]

Efficient On-the-fly Category Retrieval using ConvNets and GPUs

[http://arxiv.org/abs/1407.4764]

2015

Learning visual similarity for product design with convolutional neural networks SIGGRAPH 2015

[http://www.cs.cornell.edu/~kb/publications/SIG15ProductNet.pdf\]

Exploiting Local Features from Deep Networks for Image Retrieval

[https://arxiv.org/abs/1504.05133]

Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network ICCV 2015

[http://arxiv.org/abs/1505.07922]

Where to Buy It: Matching Street Clothing Photos in Online Shops ICCV 2015

[http://www.tamaraberg.com/papers/street2shop.pdf]

Aggregating Deep Convolutional Features for Image Retrieval

[http://arxiv.org/abs/1510.07493]

Particular object retrieval with integral max-pooling of CNN activations

[https://arxiv.org/abs/1511.05879]

2016

Deep Image Retrieval: Learning global representations for image search ECCV 2016

[https://arxiv.org/abs/1604.01325]

Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks. CVPR 2016

[http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Lin_Learning_Compact_Binary_CVPR_2016_paper.pdf\]

Fast Training of Triplet-based Deep Binary Embedding Networks. CVPR 2016

[http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhuang_Fast_Training_of_CVPR_2016_paper.pdf\]

Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles. CVPR 2016

[http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_Deep_Relative_Distance_CVPR_2016_paper.pdf\]

Bags of Local Convolutional Features for Scalable Instance Search. Best Poster Award at ICMR 2016.

[https://imatge-upc.github.io/retrieval-2016-icmr/]

Group Invariant Deep Representations for Image Instance Retrieval

[http://arxiv.org/abs/1601.02093]

Natural Language Object Retrieval

[http://arxiv.org/abs/1511.04164]

Faster R-CNN Features for Instance Search

[http://imatge-upc.github.io/retrieval-2016-deepvision/]

Where to Focus: Query Adaptive Matching for Instance Retrieval Using Convolutional Feature Maps

[https://arxiv.org/abs/1606.06811]

Adversarial Training For Sketch Retrieval

[http://arxiv.org/abs/1607.02748]

DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations

[http://personal.ie.cuhk.edu.hk/~lz013/projects/DeepFashion.html\]

CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples

[http://cmp.felk.cvut.cz/~radenfil/projects/siamac.html\]

PicHunt: Social Media Image Retrieval for Improved Law Enforcement

[http://arxiv.org/abs/1608.00905]

The Sketchy Database: Learning to Retrieve Badly Drawn Bunnies

[http://sketchy.eye.gatech.edu/]

End-to-end Learning of Deep Visual Representations for Image Retrieval

[http://www.xrce.xerox.com/Research-Development/Computer-Vision/Learning-Visual-Representations/Deep-Image-Retrieval]

What Is the Best Practice for CNNs Applied to Visual Instance Retrieval?

[https://arxiv.org/abs/1611.01640]

2017

AMC: Attention guided Multi-modal Correlation Learning for Image Search. CVPR 2017

[https://arxiv.org/abs/1704.00763]

Deep image representations using caption generators. ICME 2017

[https://arxiv.org/abs/1705.09142]

One-Shot Fine-Grained Instance Retrieval. ACM MM 2017

[https://arxiv.org/abs/1707.00811]

Selective Deep Convolutional Features for Image Retrieval. ACM MM 2017

[https://arxiv.org/abs/1707.00809]

Deep Binaries: Encoding Semantic-Rich Cues for Efficient Textual-Visual Cross Retrieval. ICCV 2017

[https://arxiv.org/abs/1708.02531]

Image2song: Song Retrieval via Bridging Image Content and Lyric Words. ICCV 2017

[https://arxiv.org/abs/1708.05851]

SIFT Meets CNN: A Decade Survey of Instance Retrieval

[http://arxiv.org/abs/1608.01807]

Image Retrieval with Deep Local Features and Attention-based Keypoints

[https://arxiv.org/abs/1612.05478]

综述

Recent Advance in Content-based Image Retrieval: A Literature Survey. Wengang Zhou, Houqiang Li, and Qi Tian 2017

[https://arxiv.org/pdf/1706.06064.pdf]

Intelligent Image Retrieval Techniques: A Survey 2014

[http://www.sciencedirect.com/science/article/pii/S1665642314716098]

A survey on content based image retrieval. 2013

[http://ieeexplore.ieee.org/document/6496719/]

Tutorial

CVPR’16 Tutorial on Image Tag Assignment, Refinement and Retrieval

[http://www.lambertoballan.net/2016/06/cvpr16-tutorial-image-tag-assignment-refinement-and-retrieval/]

Content-based image retrieval tutorial by Joani Mitro

[https://arxiv.org/pdf/1608.03811.pdf]

Tutorial on Image Retrieval System, (IRS)

[http://da.biostr.washington.edu/~sigdemos/tutorial/tutorial.pdf\]

视频教程

Deep Image Retrieval: Learning global representations for image search

[https://www.youtube.com/watch?v=yT52xDML6ys]

Image Instance Retrieval: Overview of state-of-the-art

[https://www.youtube.com/watch?v=EYq-rpaZn1o]

代码

Neural Codes for Image Retrieval

[https://github.com/arbabenko/Spoc]

Natural Language Object Retrieval

[https://github.com/andrewliao11/Natural-Language-Object-Retrieval-tensorflow]

Bags of Local Convolutional Features for Scalable Instance Search

[https://github.com/imatge-upc/retrieval-2016-icmr]

Faster R-CNN Features for Instance Search

[https://github.com/imatge-upc/retrieval-2016-deepvision]

CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples

[http://ptak.felk.cvut.cz/personal/radenfil/siamac/siaMAC_code.tar.gz\]

Class-Weighted Convolutional Features for Visual Instance Search

[https://github.com/imatge-upc/retrieval-2017-cam]

领域专家

Hervé Jégou

[http://people.rennes.inria.fr/Herve.Jegou/]

Andrew Zisserman

[https://www.robots.ox.ac.uk/~az/\]

Qi Tian

[http://www.cs.utsa.edu/~qitian/\]

Artem Babenko

[https://www.hse.ru/en/org/persons/133709478]

Datasets

Corel 1000 and 10,000 图像数据库

[http://wang.ist.psu.edu/docs/related/]

The COREL Database for Content based Image Retrieval

[https://sites.google.com/site/dctresearch/Home/content-based-image-retrieval]

Corel-5K and Corel -10K Datasets该页面下面给出了图片的链接，可以用python写个脚本把它们爬下来。

[http://www.ci.gxnu.edu.cn/cbir/Dataset.aspx]

INSTRE，中科院计算所弄的一个数据库28543张图片，还有他们做的web检索系统ISIA。

[http://vipl.ict.ac.cn/isia/instre/]

MIRFLICKR 1M数据库，100多g.

[http://press.liacs.nl/mirflickr/mirdownload.html]

Image Similarity Triplet Dataset

[http://users.eecs.northwestern.edu/~jwa368/my_data.html\]

INRIA Holidays 该数据集是Herve Jegou研究所经常度假时拍的图片（风景为主），一共1491张图，500张query（一张图一个group）和对应着991张相关图像，已提取了128维的SIFT点4455091个，visual dictionaries来自Flickr60K.

[http://lear.inrialpes.fr/~jegou/data.php\]

Oxford Buildings Dataset，5k Dataset images，有5062张图片，是牛津大学VGG小组公布的，在基于词汇树做检索的论文里面，这个数据库出现的频率极高。

[http://www.robots.ox.ac.uk/~vgg/data/oxbuildings/\]

Oxford Paris，The Paris Dataset，oxford的VGG组从Flickr搜集了6412张巴黎旅游图片，包括Eiffel Tower等。

[http://www.robots.ox.ac.uk/~vgg/data/parisbuildings/\]

201Books and CTurin180 The CTurin180 and 201Books Data Sets，2011.5，Telecom Italia提供于Compact Descriptors for Visual Search，该数据集包括：Nokia E7拍摄的201本书的封面图片（多视角拍摄，各6张），共1.3GB； Turin市180个建筑的视频图像，拍摄的camera有Galaxy S、iPhone 3、Canon A410、Canon S5 IS，共2.7GB

[http://pacific.tilab.com/www/datasets/]

Stanford Mobile Visual Search，Stanford Mobile Visual Search Dataset，2011.2，stanford提供，包括8种场景，如CD封面、油画等，每组相关图片都是采自不同相机（手机），所有场景共500张图；以后又发布了一个patch数据集，Compact Descriptors for Visual Search Patches Dataset，校对了相同patch。