专栏首页专知【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)

【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)

  • 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

  1. 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\]
  2. Google 图片搜索的原理是什么?
    • [https://www.zhihu.com/question/19726630]
  3. 基于内容的图像检索技(CBIR)术相术介绍
    • [http://blog.csdn.net/kezunhai/article/details/11614989]
  4. 图像检索:基于内容的图像检索技术
    • [http://yongyuan.name/blog/cbir-technique-summary.html]
  5. 基于内容的图像检索技术
    • [http://www.cs.cmu.edu/~juny/Prof/papers/Part2-CBIR.pdf\]
  6. 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]
  7. 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

  1. Using Very Deep Autoencoders for Content-Based Image Retrieval
    • [https://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf\]

2013

  1. Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data
    • [http://arxiv.org/abs/1312.4740]

2014

  1. Neural Codes for Image Retrieval
    • [http://arxiv.org/abs/1404.1777]
  2. Efficient On-the-fly Category Retrieval using ConvNets and GPUs
    • [http://arxiv.org/abs/1407.4764]

2015

  1. Learning visual similarity for product design with convolutional neural networks SIGGRAPH 2015
    • [http://www.cs.cornell.edu/~kb/publications/SIG15ProductNet.pdf\]
  2. Exploiting Local Features from Deep Networks for Image Retrieval
    • [https://arxiv.org/abs/1504.05133]
  3. Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network ICCV 2015
    • [http://arxiv.org/abs/1505.07922]
  4. Where to Buy It: Matching Street Clothing Photos in Online Shops ICCV 2015
    • [http://www.tamaraberg.com/papers/street2shop.pdf]
  5. Aggregating Deep Convolutional Features for Image Retrieval
    • [http://arxiv.org/abs/1510.07493]
  6. Particular object retrieval with integral max-pooling of CNN activations
    • [https://arxiv.org/abs/1511.05879]

2016

  1. Deep Image Retrieval: Learning global representations for image search ECCV 2016
    • [https://arxiv.org/abs/1604.01325]
  2. 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\]
  3. 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\]
  4. 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\]
  5. Bags of Local Convolutional Features for Scalable Instance Search. Best Poster Award at ICMR 2016.
    • [https://imatge-upc.github.io/retrieval-2016-icmr/]
  6. Group Invariant Deep Representations for Image Instance Retrieval
    • [http://arxiv.org/abs/1601.02093]
  7. Natural Language Object Retrieval
    • [http://arxiv.org/abs/1511.04164]
  8. Faster R-CNN Features for Instance Search
    • [http://imatge-upc.github.io/retrieval-2016-deepvision/]
  9. Where to Focus: Query Adaptive Matching for Instance Retrieval Using Convolutional Feature Maps
    • [https://arxiv.org/abs/1606.06811]
  10. Adversarial Training For Sketch Retrieval
    • [http://arxiv.org/abs/1607.02748]
  11. DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations
    • [http://personal.ie.cuhk.edu.hk/~lz013/projects/DeepFashion.html\]
  12. CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
    • [http://cmp.felk.cvut.cz/~radenfil/projects/siamac.html\]
  13. PicHunt: Social Media Image Retrieval for Improved Law Enforcement
    • [http://arxiv.org/abs/1608.00905]
  14. The Sketchy Database: Learning to Retrieve Badly Drawn Bunnies
    • [http://sketchy.eye.gatech.edu/]
  15. 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]
  16. What Is the Best Practice for CNNs Applied to Visual Instance Retrieval?
    • [https://arxiv.org/abs/1611.01640]

2017

  1. AMC: Attention guided Multi-modal Correlation Learning for Image Search. CVPR 2017
    • [https://arxiv.org/abs/1704.00763]
  2. Deep image representations using caption generators. ICME 2017
    • [https://arxiv.org/abs/1705.09142]
  3. One-Shot Fine-Grained Instance Retrieval. ACM MM 2017
    • [https://arxiv.org/abs/1707.00811]
  4. Selective Deep Convolutional Features for Image Retrieval. ACM MM 2017
    • [https://arxiv.org/abs/1707.00809]
  5. Deep Binaries: Encoding Semantic-Rich Cues for Efficient Textual-Visual Cross Retrieval. ICCV 2017
    • [https://arxiv.org/abs/1708.02531]
  6. Image2song: Song Retrieval via Bridging Image Content and Lyric Words. ICCV 2017
    • [https://arxiv.org/abs/1708.05851]
  7. SIFT Meets CNN: A Decade Survey of Instance Retrieval
    • [http://arxiv.org/abs/1608.01807]
  8. Image Retrieval with Deep Local Features and Attention-based Keypoints
    • [https://arxiv.org/abs/1612.05478]

综述

  1. 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]
  2. Intelligent Image Retrieval Techniques: A Survey 2014
    • [http://www.sciencedirect.com/science/article/pii/S1665642314716098]
  3. A survey on content based image retrieval. 2013
    • [http://ieeexplore.ieee.org/document/6496719/]

Tutorial

  1. CVPR’16 Tutorial on Image Tag Assignment, Refinement and Retrieval
    • [http://www.lambertoballan.net/2016/06/cvpr16-tutorial-image-tag-assignment-refinement-and-retrieval/]
  2. Content-based image retrieval tutorial by Joani Mitro
    • [https://arxiv.org/pdf/1608.03811.pdf]
  3. Tutorial on Image Retrieval System, (IRS)
    • [http://da.biostr.washington.edu/~sigdemos/tutorial/tutorial.pdf\]

视频教程

  1. Deep Image Retrieval: Learning global representations for image search
    • [https://www.youtube.com/watch?v=yT52xDML6ys]
  2. Image Instance Retrieval: Overview of state-of-the-art
    • [https://www.youtube.com/watch?v=EYq-rpaZn1o]

代码

  1. Neural Codes for Image Retrieval
    • [https://github.com/arbabenko/Spoc]
  2. Natural Language Object Retrieval
    • [https://github.com/andrewliao11/Natural-Language-Object-Retrieval-tensorflow]
  3. Bags of Local Convolutional Features for Scalable Instance Search
    • [https://github.com/imatge-upc/retrieval-2016-icmr]
  4. Faster R-CNN Features for Instance Search
    • [https://github.com/imatge-upc/retrieval-2016-deepvision]
  5. CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
    • [http://ptak.felk.cvut.cz/personal/radenfil/siamac/siaMAC_code.tar.gz\]
  6. Class-Weighted Convolutional Features for Visual Instance Search
    • [https://github.com/imatge-upc/retrieval-2017-cam]

领域专家

  1. Hervé Jégou
    • [http://people.rennes.inria.fr/Herve.Jegou/]
  2. Andrew Zisserman
    • [https://www.robots.ox.ac.uk/~az/\]
  3. Qi Tian
    • [http://www.cs.utsa.edu/~qitian/\]
  4. Artem Babenko
    • [https://www.hse.ru/en/org/persons/133709478]

Datasets

  1. Corel 1000 and 10,000 图像数据库
    • [http://wang.ist.psu.edu/docs/related/]
  2. The COREL Database for Content based Image Retrieval
    • [https://sites.google.com/site/dctresearch/Home/content-based-image-retrieval]
  3. 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]
  4. 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/]
  5. MIRFLICKR 1M数据库,100多g.
    • [http://press.liacs.nl/mirflickr/mirdownload.html]
  6. Image Similarity Triplet Dataset
    • [http://users.eecs.northwestern.edu/~jwa368/my_data.html\]
  7. 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\]
  8. 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/\]
  9. 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/\]
  10. 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/]
  11. 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。
    • [https://purl.stanford.edu/rb470rw0983]
  12. UKBench,UKBench database,2006.7,Henrik Stewénius在他CVPR06文章中提供的数据集,图像都为640x480,每个group有4张图,文件接近2GB,提供visual words。
    • [http://vis.uky.edu/~stewe/ukbench/\]

本文分享自微信公众号 - 专知(Quan_Zhuanzhi),作者:专知内容组

原文出处及转载信息见文内详细说明,如有侵权,请联系 yunjia_community@tencent.com 删除。

原始发表时间:2017-11-15

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