AbstractWe present a new method for image feature-extraction for learning image classification. An image is represented by a feature vector of distances that measure the dissimilarity between regions of the image and a set of fixed image prototypes. The method uses a text-based representation of images where the texture of an image corresponds to patterns of symbols in the text string. The distance between two images is based on the LZ-complexity of their corresponding strings. Given a set of input images, the algorithm produces cases that can be used by any supervised or unsupervised learning algorithm to learn image classification or clustering. A main advantage in this approach is the lack of need for any image processing or image analysis. A non-expert user can define the image-features by selecting a few small images that serve as prototypes for each class category. The algorithm is designed to run on a parallel processing platform. Results on the classification accuracy and processing speed are reported for several image classification problems including aerial imaging.
Belousov A. Massively parallel computations for image classification. Master's thesis, Ariel University, http://www.ariel.ac.il/sites/ratsaby/Theses/alex.pdf, 2015.
Belousov A, Ratsaby J. Massively parallel computations of the LZ-complexity of strings,. In Proc. of the 28th IEEE Convention of Electrical and Electronics Engineers in Israel (IEEEI'14), pages pp. 1-5, Eilat, Dec. 3-5 2014. https://doi.org/10.1109/EEEI.2014.7005885
Belousov A, Ratsaby J. A parallel distributed processing algorithm for image feature extraction. In Advances in Intelligent Data Analysis XIV - 14th International Symposium, IDA 2015, Saint-Etienne, France, October 22-24, 2015. Proceedings, volume 9385 of Lecture Notes in Computer Science. Springer, 2015.
Cheng H. D. Shan J, Ju W, Guo Y, Zhang L. Automated breast cancer detection and classification using ultrasound images: A survey. Pattern Recognition 2010; 43(1): 299-317. https://doi.org/10.1016/j.patcog.2009.05.012
Chester U, Ratsaby J. Universal distance measure for images. In Proceedings of the 27th IEEE Convention of Electrical Electronics Engineers in Israel (IEEEI’12), pages 1- 4, Eilat, Israel, November 14-17, 2012. https://doi.org/10.1109/EEEI.2012.6377115
Chester U, Ratsaby J. Machine learning for image classification and clustering using a universal distance measure. In Brisaboa N, Pedreira O, Zezula P, Eds., Proceedings of the 6th International Conference on Similarity Search and Applications (SISAP’13), volume 8199 of Springer Lecture Notes in Computer Science 2013; pp. 59- 72. https://doi.org/10.1007/978-3-642-41062-8_7
Cilibrasi R, Vitanyi P. Clustering by compression. IEEE Transactions on Information Theory 2005; 51(4): 1523-1545. https://doi.org/10.1109/TIT.2005.844059
Fei-Fei L, Fergus R, Perona P. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Image Understanding 2007; 106(1): 59-70. Special issue on Generative Model Based Vision. https://doi.org/10.1016/j.cviu.2005.09.012
Galar M, Derrac J, Peralta D, Triguero I, Paternain D, Lopez- Molina C, Garc´ıa S, Ben´ıtez J. M. Pagola M, Barrenechea E, Bustince H, Herrera F. A survey of fingerprint classification part i: Taxonomies on feature extraction methods and learning models. Knowledge-Based Systems 2015; 81: 76- 97. https://doi.org/10.1016/j.knosys.2015.02.008
Gonzalez RC, Woods R. E. Digital Image Processing (3rd Edition). Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 2006.
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten I. H. The WEKA data mining software: An update. SIGKDD Explorations 2009; 11(1): 10-18. https://doi.org/10.1145/1656274.1656278
Haralick R. M. Shanmugam K, Dinstein I. Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions on, SMC 1973; 3(6): 610-621. https://doi.org/10.1109/TSMC.1973.4309314
Lazebnik S, Schmid C, Ponce J. A sparse texture representation using local affine regions. IEEE Trans Pattern Anal Mach Intell 2005; 27(8): 1265-1278. https://doi.org/10.1109/TPAMI.2005.151
Lu D, Weng Q. A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 2007; 28(5): 823-870. https://doi.org/10.1080/01431160600746456
Ojala T, Pietikainen M, Harwood D. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 1996; 29(1): 51-59. https://doi.org/10.1016/0031-3203(95)00067-4
Pham D.T. Alcock RJ. Chapter 5 -classification. In D.T. PhamR.J. Alcock, editor, Smart Inspection Systems, Academic Press, London 2003; pp. 129-155. https://doi.org/10.1016/B978-012554157-2/50005-5
Raju J, Durai C. A. D. A survey on texture classification techniques. In Information Communication and Embedded Systems (ICICES), 2013 International Conference on, 2013; pp. 180-184. https://doi.org/10.1109/ICICES.2013.6508183
Risojevic V, Babic Z. Aerial image classification using structural texture similarity. In Proceedings of the IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) 2011; pp. 190-195. https://doi.org/10.1109/ISSPIT.2011.6151558
Sayood K, Otu H. H. A new sequence distance measure for phylogenetic tree construction. Bioinformatics 2003; 19(16): 2122-2130. https://doi.org/10.1093/bioinformatics/btg295
Ziv J, Lempel A. On the complexity of finite sequences. IEEE Transactions on Information Theory 1976; 22(3): 75-81. https://doi.org/10.1109/TIT.1976.1055501