繼上篇：https://blog.csdn.net/timcanby/article/details/104382103
今天繼續對：
Section 6 底層特徵的融合運用
Section 7 局部特徵
Section 8 基於深度學習的種種
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Section 6 底層特徵的融合運用
[55] R. Ashraf, M. Ahmed, S. Jabbar et al., “Content based image retrieval by using color descriptor and discrete wavelet transform,” Journal of Medical Systems, vol. 42, no. 3, p. 44,2018.
這個的模型基於discrete wavelet transform (DWT)和顏色，使用了顏色（RGB 和 YCbCr）紋理和形狀特徵（Canny 邊緣算子），檢索用的ANN
[56] R. Ashraf, M. Ahmed, U. Ahmad, M. A. Habib, S. Jabbar, and K. Naseer, “MDCBIR-MF: multimedia data for content based image retrieval by using multiple features,” Multimedia Tools and Applications, pp. 1–27, 2018.
這個用了顏色和紋理特徵，這篇處理顏色信息的color moment有點意思
[57] Y. Mistry, D. Ingole, and M. Ingole, “Content based image retrieval using hybrid features and various distance metric,” Journal of Electrical Systems and Information Technology,vol. 5, no. 3, pp. 878–888, 2017.
這篇總結了很多距離計算的方法的區別，可以看看
[58] K. T. Ahmed, M. A. Iqbal, and A. Iqbal, “Content based image retrieval using image features information fusion,”Information Fusion, vol. 51, pp. 76–99, 2018.
這篇文章用了顏色特徵和邊緣特徵，然後用BoVW來檢索
[59] P. Liu, J.-M. Guo, K. Chamnongthai, and H. Prasetyo,“Fusion of color histogram and LBP-based features for texture image retrieval and classification,” Information Sciences,vol. 390, pp. 95–111, 2017.
這個用了LBP（local base pattern），和顏色特徵
[60] W. Zhou, H. Li, J. Sun, and Q. Tian, “Collaborative index embedding for image retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5,pp. 1154–1166, 2018.
這篇用了來自cnn的中間層輸出的深度特徵和SIFT特徵進行Collaborative Index Embedding然後檢索
[61] C. Li, Y. Huang, and L. Zhu, “Color texture image retrieval based on Gaussian copula models of Gabor wavelets,” Pattern Recognition, vol. 64, pp. 118–129, 2017.
如題這個用了Gabor wavelet
[62] H. H. Bu, N. Kim, C. J. Moon, and J. H. Kim, “Content-based image retrieval using combined color and texture features extracted by multi-resolution multi-direction filtering,”Journal of Information Processing Systems, vol. 13, no. 3, pp. 464–475, 2017.
這篇是用Multi-Resolution Multi-Directional (MRMD) filters來結合了顏色和紋理特徵
[63] A. Nazir, R. Ashraf, T. Hamdani, and N. Ali, “Content based image retrieval system by using HSV color histogram,discrete wavelet transform and edge histogram descriptor,” in Proceedings of the 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), pp. 1–6, IEEE, Sukkur, Pakistan,March 2018.
這個就是如題所示，用了HSV color histogram,discrete wavelet transform and edge histogram descriptor做了很多對比實驗
然後Section 7 基於局部特徵的檢索
這個比較長，慢慢來
講這個之前需要先講一下接下來論文很多都會用到的：稀疏表示（sparse representation），這邊參考一下這篇博客：
https://blog.csdn.net/Forever_pupils/article/details/88572281
還有這篇論文：
C. Celik and H. S. Bilge, “Content based image retrieval with sparse representations and local feature descriptors: a comparative study,” Pattern Recognition, vol. 68, pp. 1–13,2017.
https://www.sciencedirect.com/science/article/pii/S0031320317301048
[64] L.-W. Kang, C.-Y. Hsu, H.-W. Chen, C.-S. Lu, C.-Y. Lin, andS.-C. Pei, “Feature-based sparse representation for image similarity assessment,” IEEE Transactions on Multimedia,vol. 13, no. 5, pp. 1019–1030, 2011.
這篇裏面有個比較關鍵的點叫：K-SVD dictionary learning algorithm 來自於：M. Aharon, M. Elad, and A. M. Bruckstein, “The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process., vol. 54, no. 11, pp. 4311–4322, Nov. 2006
[65] Z.-Q. Zhao, H. Glotin, Z. Xie, J. Gao, and X. Wu, “Cooperative sparse representation in two opposite directions for semi-supervised image annotation,” IEEE Transactions on Image Processing, vol. 21, no. 9, pp. 4218–4231, 2012.
這篇的主要就是自監督的一個模型Co-KSR的提出（然後本文介紹了一些有名的自監督分類器比如TSVM, GFHF, LGC）
[66] J. J. )iagarajan, K. N. Ramamurthy, P. Sattigeri, and A. Spanias, “Supervised local sparse coding of sub-image features for image retrieval,” in Proceedings of the 2012 19th IEEE International Conference on Image Processing (ICIP),pp. 3117–3120, IEEE, Melbourne, Australia, September-October 2012.
這篇是supervised的
[67] C. Hong and J. Zhu, “Hypergraph-based multi-example ranking with sparse representation for transductive learning image retrieval,” Neurocomputing, vol. 101, pp. 94–103, 2013.
（https://reader.elsevier.com/reader/sd/pii/S0925231212006443?token=9E90AD2CBD6B406E1CB15A16D7A9CC86ABF6C996BF6C9FA52777AE88BF4C21E7ACE9081BBB19E89641C1030417291FAA）這篇推薦一下主要是解決了多圖同時處理節約了計算時間的問題而且舉例和表達都相對易懂。
[68] D. Wang, S. C. Hoi, Y. He, J. Zhu, T. Mei, and J. Luo,“Retrieval-based face annotation by weak label regularized local coordinate coding,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 3, pp. 550–563,2014
這個是人臉分類上的運用
[69] M. Srinivas, R. R. Naidu, C. S. Sastry, and C. K. Mohan,“Content based medical image retrieval using dictionary learning,” Neuro computing, vol. 168, pp. 880–895, 2015.
K-SVD拿來聚類詞典
[70] S. Mohamadzadeh and H. Farsi, “Content-based image retrieval system via sparse representation,” IET Computer
Vision, vol. 10, no. 1, pp. 95–102, 2016.
這篇好處是對比了很多現有的方法
[71] Q. Li, Y. Han, and J. Dang, “Sketch4Image: a novel framework for sketch-based image retrieval based on product quantization with coding residuals,” Multimedia Tools and Applications, vol. 75, no. 5, pp. 2419–2434, 2016.
這篇很好玩，基於sketch圖像的，重視手上data形狀輪廓特徵的同學有很大參考價值
[72] H. Wu, R. Bie, J. Guo, X. Meng, and S. Wang, “Sparse coding based few learning instances for image retrieval,” Multimedia Tools and Applications, vol. 78, no. 5, pp. 6033–6047, 2018.
這篇組合了cross-validation sparse coding representation, sparse coding-based instance distance, 和 improved KNN model
[73] Y. Duan, J. Lu, J. Feng, and J. Zhou, “Context-aware local binary feature learning for face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40,no. 5, pp. 1139–1153, 2018.
這篇的特色在於取得了robust local binary features的提案，也就是對Binary Feature Descriptor的討論，非常的推薦閱讀一下，地址：https://ieeexplore.ieee.org/document/7936534
然後來個評價試驗的結果：
然後作者總結了下基於ML經常用於CBIR的方法（如下圖）：
接下來是很多同學最喜愛的，基於深度學習：
Section 8 基於深度學習的種種
[82] N. Kondylidis, M. Tzelepi, and A. Tefas, “Exploiting tf-idf in deep convolutional neural networks for content based image retrieval,” Multimedia Tools and Applications, vol. 77, no. 23, pp. 30729–30748, 2018.
這個把操作文本的tf-idf嵌入了CNN框架裏使用的基礎框架是VGG16
[83] X. Shi, M. Sapkota, F. Xing, F. Liu, L. Cui, and L. Yang,“Pairwise based deep ranking hashing for histopathology image classification and retrieval,” Pattern Recognition,vol. 81, pp. 14–22, 2018.
這篇作者提出了 deep ranking hashing來對醫學圖像進行01編碼
[84] L. Zhu, J. Shen, L. Xie, and Z. Cheng, “Unsupervised visual hashing with semantic assistant for content-based image retrieval,” IEEE Transactions on Knowledge and Data Engineering,vol. 29, no. 2, pp. 472–486, 2017.
https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4813&context=sis_research
🌟offline learning and online learning 的 Unsupervised學習，這篇文章強烈的標小星星啊，結合了圖像的wiki文本描述，把語義和本體內容融合起來提出了一種編碼的方法
[85] A. Alzu’bi, A. Amira, and N. Ramzan, “Content-based image retrieval with compact deep convolutional features,” Neurocomputing,vol. 249, pp. 95–105, 2017.
然後這個使用了深度卷積特徵的
[89] A. Karpathy and L. Fei-Fei, “Deep visual-semantic alignments for generating image descriptions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,pp. 3128–3137, Boston, MA, USA, June 2015.
然後其實檢索的一大挑戰就是處理具有很多隱藏語義的複雜圖像，🌟這是李飛飛的經典之作之一了，生成圖像描述的（https://cs.stanford.edu/people/karpathy/cvpr2015.pdf）
[92] C. Zhang, J. Cheng, and Q. Tian, “Multiview, few-labeled object categorization by predicting labels with view consistency,”IEEE Transactions
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8398481
然後基於深度學習基礎的我覺得這篇文章寫的太跳躍了之後新開一篇來寫😂😂😂😂
剩餘的將會在下一篇博客（下）裏更新，重點講解
本次兩塊的內容，如果有什麼錯誤歡迎指正留言～～
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個人github：https://github.com/timcanby