Abstract
Content-based image retrieval (CBIR) is retrieving relevant images from the large image database through visual characteristics. Each image in the database and query image is represented through feature vector derived from color and texture features in the image. These feature vectors are compared for relevance to obtain similar images in CBIR system. Therefore, length of the feature vector is very important in the CBIR system. Higher length of the feature vector increases number of comparison and in turn, increases the computational complexity, whereas lower length of the feature vector reduces comparison and complexity. In this paper, performance of the proposed CBIR system using color and texture feature extraction through histogram and Gabor wavelet transform, respectively, is presented. It is necessary to extract all the features of each image from image database and query images. These features are further presented for ant colony optimization to reduce the length of the feature vector. These final features are used in image retrieval process. Experiment results clearly show that the proposed CBIR system through ant colony optimization algorithm performance is better than other algorithms by 1.8% with respect to precision and recall. Also, the proposed algorithm clearly demonstrates the improvement by 10% on the precision and recall using only color and texture features. One of the biggest advantage and improvement was reduction in retrieval time in comparison with the other algorithms.
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References
Mahamuni, C.V., Wagh, N.B.: Study of CBIR methods for retrieval of digital images based on colour and texture extraction. In: IEEE International Conference on Computer Communication and Informatics, Coimbatore, India (2017)
Gerard, S., Buckely, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1998)
Chen, Y., Wang, J.: Image categorization by learning and reasoning with regions. J. Mach. Learn. Res. 5, 913–939 (2004)
Long, F., Zhang, H., Dagan, H., Feng, D.: Fundamentals of content based image retrieval. In: Multimedia Information Retrieval and Management. Technological Fundamentals and Applications, Multimedia Signal Processing Book, Chapter 1. Springer, Berlin, Heidelberg New York, pp. 1–26 (2003)
Manjunath, B., Ma, W.: Texture features for Browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996)
Gevers, T., Smeulders, A.: Pictoseek: combining color and shape invariant features for image retrieval. IEEE Trans. Image Process. 9(1), 102–119 (2001)
Muhammad Fachrurrozi, E., Saparudin, M.: Multi-object face recognition using content based image retrieval (CBIR). In: IEEE International Conference on Electrical Engineering and Computer Science, Indonesia (2017)
Fuertes, J., Lucena, M., Perez, N., Martinez, J.: A scheme of color image retrieval from databases. Pattern Recogn. Lett. 22, 323–337 (2001)
Ouyang, A., Tan, Y.: A novel multi-scale spatial-color descriptor for content based image retrieval. In: Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, Mexico, vol. 3, pp. 1204–1209 (2002)
Yu, H., Li, M., Zhang, H., Feng, J.: Color texture moments for content-based image retrieval. In: Proceedings of the International Conference on Image Processing, Rochester, New York, USA, Sept 22–25, vol. 3, pp. 929–932 (2002)
Guan, H., Wada, S.: Flexible color texture retrieval method using multiresolution mosaic for image classification. In: Proceedings of the 6th International Conference on Signal Processing, vol. 1, Feb 2002, pp. 612–615
Lew, M., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: state of the art and challenges. ACM Transactions on Multimedia Computing, Communications and Applications 2(1), 1–19 (2006)
Jain, N., Salankar, S.S.: Performance estimation of relevance feedback for content based image retrieval system. In: IEEE International Conference on Electrical, Electronics, Computers, Communication, Mechanical and Computing, Vellore, India (2018)
Jain, N., Salankar, S.S.: Content based image retrieval using improved gabor wavelet transform and linear discriminant analysis. In: IEEE International Conference on Convergence in Technology, Pune, India (2018)
Yue, J., Li, Z., Liu, L., Fu, Z.: Content-based image retrieval using colour and texture fused features. In: Mathematical Computational Modeling, vol. 54, pp. 1121–1127 (2011)
Jalab, H.A.: Image retrieval system based on colour layout descriptor and Gabor filters. In: IEEE Conference on Open System (ICOS) Langkawi, Malaysia (2011)
Rahimi, M., Moghaddam, M.E.: A content based image retrieval system based on colour ton distributed descriptors. In: Signal Image and Video Processing, (SIViP), vol. 9, pp. 691–704 (2013)
Singh, V.P., Malhotra, S., Srivastava, R.: Combining hybrid information descriptors and DCT for improved CBIR performance. In: International Conference on Control, Computing, Communication and Materials (ICCCCM), Allahabad, pp. 1–5 (2016)
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Jain, N., Salankar, S.S. (2019). Content-Based Image Retrieval Using Color and Texture Features Through Ant Colony Optimization. In: Iyer, B., Nalbalwar, S., Pathak, N. (eds) Computing, Communication and Signal Processing . Advances in Intelligent Systems and Computing, vol 810. Springer, Singapore. https://doi.org/10.1007/978-981-13-1513-8_104
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DOI: https://doi.org/10.1007/978-981-13-1513-8_104
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