CBIR_EXPLORE

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对图像检索技术进行探索

CBIR_EXPLORE 최신버젼 다운로드

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# CBIR_EXPLORE  
This repository is built to explore Content-Based-Image-Retrieval technology.  
This page shows related codes, papers and other informations.  
# Overview:
- [Guide-CBIR](https://github.com/insikk/Guide-CBIR)
- [CBIR_LeaderBoard](https://github.com/insikk/CBIR_LeaderBoard)
- https://github.com/willard-yuan/willard-yuan.github.io/tree/master/_posts
---
# Team
- 淘宝任小枫团队
# Code:
- [ReferenceCode: cnn-cbir-benchmark](https://github.com/willard-yuan/cnn-cbir-benchmark)
- [ReferenceCode: image-retrieval](https://github.com/willard-yuan/image-retrieval)
- [ReferenceCode: practical-cbir-handbook](https://github.com/willard-yuan/practical-cbir-handbook)
- [ReferenceCode: Learn-to-identify-similar-images](https://github.com/keloli/Learn-to-identify-similar-images)
- [ReferenceCode: CNN-for-Image-Retrieval](https://github.com/willard-yuan/CNN-for-Image-Retrieval)
- [ReferenceCode: hashing-baseline-for-image-retrieval](https://github.com/willard-yuan/hashing-baseline-for-image-retrieval)
- [ReferenceCode: sift-match-with-ransac](https://github.com/willard-yuan/sift-match-with-ransac)
- [ReferenceCode: py-cbir-image-search-engine](https://github.com/willard-yuan/py-cbir-image-search-engine)
---
# Papers:
- [**Paper: SIFT Meets CNN: A Decade Survey of Instance Retrieval**](https://arxiv.org/abs/1608.01807)
- [Paper: Three things everyone should know to improve object retrieval](https://www.robots.ox.ac.uk/%7Evgg/publications/2012/Arandjelovic12/arandjelovic12.pdf)
- [Paper: awesome-cbir-papers](https://github.com/willard-yuan/awesome-cbir-papers)
- [Paper: Fine-tuning CNN Image Retrieval with No Human Annotation](https://arxiv.org/abs/1711.02512)
- [Paper: End-to-end Learning of Deep Visual Representations for Image Retrieval](https://arxiv.org/abs/1610.07940)
---
# Sparse Coding:
- [总结SPM，ScSPM，LLC](http://blog.csdn.net/wertsdfg/article/details/50015929)
- [稀疏编码及其改进（ScSPM，LLC，super-vector coding）](http://blog.csdn.net/tiandijun/article/details/40681463)
- [图像的稀疏表示——ScSPM和LLC的总结](http://blog.csdn.net/jwh_bupt/article/details/9837555)

For more details:[CBIR EXPLORE](https://www.jianshu.com/p/26f68f17f078)

CBIR_EXPLORE

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