Content-Based Image Retrieval Using HSV Color Space Features
In this paper, a method is provided for content-based image retrieval. Content-based image retrieval system searches query an image based on its visual content in an image database to retrieve similar images. In this paper, with the aim of simulating the human visual system sensitivity to image's edges and color features, the concept of color difference histogram (CDH) is used. CDH includes the perceptually color difference between two neighboring pixels with regard to colors and edge orientations. Since the HSV color space is close to the human visual system, the CDH is calculated in this color space. In addition, to improve the color features, the color histogram in HSV color space is also used as a feature. Among the extracted features, efficient features are selected using entropy and correlation criteria. The final features extract the content of images most efficiently. The proposed method has been evaluated on three standard databases Corel 5k, Corel 10k and UKBench. Experimental results show that the accuracy of the proposed image retrieval method is significantly improved compared to the recently developed methods.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 52
 Zarchi, M. S., Monadjemi, A., & Jamshidi, K. “A concept-based model for image retrieval systems”. Computers & Electrical Engineering 2015, 46, 303-313.
 Carneiro, G., Chan, A. B., Moreno, P. J., & Vasconcelos, N. (). “Supervised learning of semantic classes for image annotation and retrieval”. IEEE transactions on pattern analysis and machine intelligence, 2007, 29(3).
 Yang, W., Yin, X., & Xia, G. S. “Learning high-level features for satellite image classification with limited labeled samples”. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(8), 4472-4482.
 Heller, K. A., & Ghahramani, Z. “A simple Bayesian framework for content-based image retrieval”. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, 2006, (Vol. 2, pp. 2110-2117). IEEE.
 Bose, S., Pal, A., Mallick, J., Kumar, S., & Rudra, P. “A Hybrid Approach for Improved Content-based Image Retrieval using Segmentation”. arXiv preprint arXiv: 2015, 1502.03215.
 Long, J., Shelhamer, E., & Darrell, T. “Fully convolutional networks for semantic segmentation”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, (pp. 3431-3440).
 Meng, X., An, Y., He, J., Zhuo, Z., Wu, H., & Gao, X. “Similar image retrieval only using one image”. Optik-International Journal for Light and Electron Optics, 2016, 127(1), 141-144.
 Xu, Y. Y. “Multiple-instance learning based decision neural networks for image retrieval and classification”. Neurocomputing, 2016, 171, 826-836.
 Zarchi, M. S., Monadjemi, A., & Jamshidi, K. “A semantic model for general purpose content-based image retrieval systems”. Computers & Electrical Engineering, 2014, 40(7), 2062-2071.
 Girija, O. K., & Elayidom, M. S. “Overview of Image Retrieval Techniques”. In International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), International Conference on Recent Trends in Computing and Communication (ICRTCC 2015) Cochin College of Engineering & Technology (Vol. 4).
 Qian, X., Tan, X., Zhang, Y., Hong, R., & Wang, M. “Enhancing sketch-based image retrieval by re-ranking and relevance feedback”. IEEE Transactions on Image Processing, 2016, 25(1), 195-208.
 Chatzichristofis, S. A., & Boutalis, Y. S. “Fcth: Fuzzy color and texture histogram-a low level feature for accurate image retrieval”. In Image Analysis for Multimedia Interactive Services, 2008. WIAMIS'08. Ninth International Workshop on, 2008, (pp. 191-196). IEEE. May,
 Neelima, N., & Reddy, E. S. “An Efficient Multi Object Image Retrieval System Using Multiple Features and SVM”. In SIRS, 2015, (pp. 257-265). Decemberm,
 Singha, M., & Hemachandran, K. “Content based image retrieval using color and texture”. Signal & Image Processing, 2012, 3(1), 39.
 Jenni, K., Mandala, S., & Sunar, M. S. “Content Based Image Retrieval using colour strings comparison”. Procedia Computer Science, 2015, 50, 374-379.
 Liu, G. H., Li, Z. Y., Zhang, L., & Xu, Y. “Image retrieval based on micro-structure descriptor”. Pattern Recognition, 2011, 44(9), 2123-2133.
 Liu, G. H., Zhang, L., Hou, Y. K., Li, Z. Y., & Yang, J. Y. “Image retrieval based on multi-texton histogram”. Pattern Recognition, 2010, 43(7), 2380-2389.
 Singh, C., & Kaur, K. P. “A fast and efficient image retrieval system based on color and texture features”. Journal of Visual Communication and Image Representation, 2016, 41, 225-238.
 Liu, G. H., & Yang, J. Y. “Content-based image retrieval using color difference histogram”. Pattern Recognition, 2013, 46(1), 188-198.
 Kastner, S., & Ungerleider, L. G. “The neural basis of biased competition in human visual cortex”. Neuropsychologia, 2001, 39(12), 1263-1276.
 Livingstone, M. S., & Hubel, D. H. “Anatomy and physiology of a color system in the primate visual cortex”. Journal of Neuroscience, 1984, 4(1), 309-356.
 Paschos, G. “Perceptually uniform color spaces for color texture analysis: an empirical evaluation”. IEEE transactions on Image Processing, 2001, 10(6), 932-937.
 Burger, W., Burge, M. J., Burge, M. J., & Burge, M. J. “Principles of digital image processing” 2009, (p. 221). London: Springer.
 Gonzalez, R. C., Woods, R. E., & Eddins, S. L. “Digital Image Processing Using MATLAB”: AND Mathworks, MATLAB Sim, 2007, SV 07.
 Julesz, B. “Textons, the elements of texture perception, and their interactions”. Nature, 1981, 290(5802), 91-97.
 Manjunath, B. S., Ohm, J. R., Vasudevan, V. V., & Yamada, A. “Color and texture descriptors”. IEEE Transactions on circuits and systems for video technology, 2001, 11(6), 703-715.
 Luo, J., & Crandall, D. “Color object detection using spatial-color joint probability functions”. IEEE Transactions on Image Processing, 2006, 15(6), 1443-1453.
 Brown, L. M. “Example-based color vehicle retrieval for surveillance”. In Advanced Video and Signal Based Surveillance (AVSS), 2010 Seventh IEEE International Conference on (pp. 91-96). IEEE. August, 2010.
 Sorkhi, A. G., Hassanpour, H., & Mazzeo, P. L. “People Re-identification in Non-Overlapping Field-of-views using Cumulative Brightness Transform Function and Body Segments in Different Color Spaces”. International Journal of Engineering-Transactions C: Aspects, 2015, 28(12), 1711.
 Corel Dataset,http://smartcbir.nph-co.ir/datasets.php.
 UKBench Dataset, http://smartcbir.nph-co.ir/datasets.php.
 Kaur, S., & Aggarwal, D. “Image Content Based Retrieval System using Cosine Similarity for Skin Disease Images”. Advances in Computer Science: an International Journal, 2013, 2(4), 89-95.
 Charulatha, B. S., Rodrigues, P., Chitralekha, T., & Rajaraman, A. “A Comparative study of different distance metrics that can be used in Fuzzy Clustering Algorithms”. International Journal of Emerging Trends and Technology in Computer Science 2013, (IJETTICS).
 Cha, S. H. “Comprehensive survey on distance/similarity measures between probability density functions”. City, 2007, 1(2), 1.