Content Based Image Retrieval (CBIR) is the technique of retrieving the similar images from the large database as per user query by matching the contents of images. CBIR is widely used in various computer vision applications such as medical field, E-commerce, Satellite Imaging, and Art Collections and so on. Different types of contents which can be used for retrieving images are Color, Texture, Shape, and/or Spatial Information. The performance of CBIR depends vitally on Feature Extraction, Feature Reduction, Feature Selection, Similarity Measure, Classification, and Ranking. This paper presents the review of different feature extraction strategies used recently for CBIR. Literature review of different Feature Extraction methods used for evaluating the performance CBIR are discussed in order to grasping details about the domain. This review article mainly focuses on the feature extraction which is most crucial part of the CBIR system.

CBIR, Feature Extraction, Color, Texture, Shape

[1]. Antani, Sameer, L Rodney Long, and George R Thoma. "Bridging the gap: Enabling CBIR in medical applications." Computer-Based Medical Systems, 21st IEEE International Symposium on. 2008. 4-6.
[2]. Chang, Che, X Yu, X Sun, and B Yu. "Image retrieval by information fusion based on scalable vocabulary tree and robust Hausdorff distance." Eurasip Journal on Advances in Signal Processing, 2017: 1-13.
[3]. Fadaei, S, R Amirfattahi, and M R Ahmadzadeh. "A New Content-Based Image Retrieval System Based on Optimized Integration of DCD, Wavelet and Curvelet Features." IET Image Processing 11, no. 2 (2017): 89-98.
[4]. Joshi, Chandani, GN Purohit, and Saurabh Mukherjee. "Impact of CBIR journey in satellite imaging." Communication and Computing Systems: Proceedings of the International Conference on Communication and Computing Systems. CRC Press, 2017. 341.
[5]. Kadobayashi, Rieko, and Katsumi Tanaka. "3D viewpoint-based photo search and information browsing." Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2005. 621-622.
[6]. Khare, Ashish, and Prashant Srivastava. "Utilizing multiscale local binary pattern for content-based image retrieval." Multimedia Tools and Applications 77, no. 10 (2017): 12377–12403.
[7]. Kumar, Arun T, and Naaz Effat. "Enhanced Content Based Image Retrieval Using Machine Learning Techniques." Innovations in Information, Embedded and Communication Systems, International Conference on. 2017. 1-12.
[8]. Liu, Y, D Zhang, G Lu, and W Y Ma. "A survey of content-based image retrieval with high-level semantics." Pattern Recognition 40, no. 1 (2007): 262-282.
[9]. Nagarajan, V, and T G Subash Kumar. "Local curve pattern for content-based image retrieval." Pattern Analysis and Applications In Press (2018): 1-10.
[10]. Rao, L. Koteswara, D. Venkata Rao, and L. Pratap Reddy. "Local mesh quantized extrema patterns for image retrieval." SpringerPlus 5, no. 1 (2016): 976-991.
[11]. Rui, Y, T S Huang, and S F Chang. "Image retrieval: Current techniques, promising directions, and open issue." Journal of Visual Communication and Image Representation 10, no. 1 (1999): 39-62.
[12]. Shriram, KV, PLK Priyadarsini, and A Baskar. "An intelligent system of content-based image retrieval for crime investigation." International Journal of Advanced Intelligence Paradigms 7, no. 3 (2015): 264--279.
[13]. Tan, Xiaoyang, and Bill Triggs. "Enhanced local texture feature sets for face recognition under difficult lighting conditions." International Workshop on Analysis and Modeling of Faces and Gestures. Springer, 2007. 168-182.
[14]. Tiwari, Ashwani Kumar, Vivek Kanhangad, and Ram Bilas Pachori. "Histogram refinement for texture descriptor based image retrieval." Signal Processing: Image Communication 53 (2017): 73-85.
[15]. Tyagi, Vipin. Content Based Image Retrieval Ideas Influences and Current Trends. Springer Nature Singapore Pte Ltd, 2017.
[16]. Zhou, Wengang, Houqiang Li, Jian Sun, and Qi Tian. "Collaborative Index Embedding for Image Retrieval." IEEE transactions on pattern analysis and machine intelligence 40, no. 5 (2018): 1154-1166.