Bag of Features or BoF approach has been used in many computer vision tasks, including image classification, video search, robot localization, and texture recognition. It is so widely popular because of its simplicity. These methods are based on unordered collections of image descriptors which are then quantized and are discarded spatial information, therefore conceptually and computationally simpler than many alternative methods, because of this; BoF based systems have set new performance standards on popular image classification benchmarks and have achieved scalability breakthroughs in image retrieval. This paper reviews related works based on the issues of improving and/or applying BoF. Emphasis is placed on recent techniques that mitigate quantization errors, improve feature detection, and speed up image retrieval. Meanwhile, unresolved issues and fundamental challenges are also raised. Among those issues the best techniques for sampling images, describing local image features, and evaluating system performance. Among those the fundamental challenges are how the BoF methods can contribute in localizing the objects in more complex images, or associating high-level semantics with natural images. Moreover, many recent works are compared in terms of the methodology of BoF feature generation and experimental design. Different Classification Models are also discussed.

Bag Of Features, Feature Extraction, Quantization, Clustering, Image Representation, Image Classification, Support Vector Machine

[1] Olivier Chapelle, Patrick Haffner, and Vladimir N. Vapnik,“Support Vector Machines for Histogram-Based Image Classification”,IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10, NO. 5, SEPTEMBER 1999.
[2] T. Dharani, I. Laurence Aroquiaraj IEEE Member “Content Based Image Retrieval System using Feature Classification with Modified KNN Algorithm”, arXiv preprint:130.4717,2013
[3] A. Bosch, X. Munoz, and R. Mart , “Which is the best way to organize/classify images by content?” Image and Vision Computing, vol. 25, no. 6, pp. 778–791, 2007.
[4] Chih-Fong Tsai, “Bag-of-Words Representation in Image Annotation: A Review”, International Scholarly Research Network, Volume 2012, Article ID 376804, 19 pages doi:10.5402/2012/376804.
[5]K. Mikolajczyk, B. Leibe, and B. Schiele, “Local features for object class recognition,” in Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV ’05), pp. 1792–1799, October 2005.
[6] T. Tuytelaars and K. Mikolajczyk, “Local invariant feature detectors: a survey,” Foundations and Trends in Computer Graphics and Vision, vol. 3, no. 3, pp. 177–280, 2007.
[7] K. Mikolajczyk, T. Tuytelaars, C. Schmid et al., “A comparison of affine region detectors,” International Journal of Computer Vision, vol. 65, no. 1-2, pp. 43–72, 2005.
[8] E. Horster and R. Lienhart, “Fusing local image descriptors ¨ for large-scale image retrieval,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ’07), pp. 1–8, June 2007.
[9] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004.
[10] D. Gokalp and S. Aksoy, “Scene classification using bag-of- ¨ regions representations,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ’07), pp. 1–8, June 2007.
[11] A. Bosch, A. Zisserman, and X. Munoz, “Scene classification via pLSA,” in European Conference on Computer Vision, pp. 517–530, 2006.
[12] J. Sivic and A. Zisserman, “Video google: a text retrieval approach to object matching in videos,” in Proceedings of the 9th IEEE International Conference on Computer Vision (ICCV ’03), pp. 1470–1477, October 2003.
[13] J. Sivic, B. C. Russell, A. A. Efros, A. Zisserman, and W. T. Freeman, “Discovering objects and their location in images,” in Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV ’05), pp. 370–377, October 2005.
[14] A. Bosch, A. Zisserman, and X. Munoz, “Scene classification via pLSA,” in European Conference on Computer Vision, pp. 517–530, 2006.
[15] F. Jurie and B. Triggs, “Creating efficient codebooks for visual recognition,” in Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV ’05), pp. 604–610, October 2005.
[16] L. Fei-Fei and P. Perona, “A bayesian hierarchical model for learning natural scene categories,” in Proceedings of the 6th IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ’05), pp. 524–531, June 2005.
[17] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004.
[18] Y. Ke and R. Sukthankar, “PCA-SIFT: a more distinctive representation for local image descriptors,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ’04), pp. 506–513, July 2004.
[19] J. R. R. Uijlings, A. W. M. Smeulders, and R. J. H. Scha, “Real-time visual concept classification,” IEEE Transactions on Multimedia, vol. 12, no. 7, pp. 665–681, 2010.
[20] K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615–1630, 2005.
[21] J. Zhang, M. Marszałek, S. Lazebnik, and C. Schmid, “Local features and kernels for classification of texture and object categories: a comprehensive study,” International Journal of Computer Vision, vol. 73, no. 2, pp. 213–238, 2007.
[22] Z. Li, Z. Shi, X. Liu, Z. Li, and Z. Shi, “Fusing semantic aspects for image annotation and retrieval,” Journal of Visual Communication and Image Representation, vol. 21, no. 8, pp. 798–805, 2010.