Abstract
Direct histogram to histogram matching in content-based image retrieval is not proficient due to its large number of bins. The total number of bins of an original histogram represents the large dimensional feature descriptor which requires high computational overhead during the retrieval process. To address this issue, in the proposed scheme image histogram is quantized into a different number of bins which represents the low dimensional feature descriptor effectively. Since, the global and local features play an important role in image retrieval, therefore, considering any single feature for image retrieval is not adequate, so in this paper, a quantized histogram-based global and local features have been considered for feature representation. To avoid variations among the feature components, suitable weights are assigned to the local and global features effectively. To check the efficacy of the proposed method, performance analysis using a different number of bins has been evaluated based on two standard similarity distances for corel-1 K image dataset. The presented work has achieved satisfactory retrieval results in terms of precision, recall, and F-score metrics.
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References
Rui Y, Huang TS, Chang S-F (1999) Image retrieval: past, present, and future. J Vis Commun Image Represent 10(1):1–23
Li X, Yang J, Ma J (2021) Recent developments of content-based image retrieval (CBIR). Neurocomputing
Rafi M, Mukhopadhyay S (2019) Salient object detection employing regional principal color and texture cues. Multimed Tools Appl 78(14):19735–19751
Latif A et al (2019) Content-based image retrieval and feature extraction: a comprehensive review. Math Problems Eng 2019
Ahmed KT, Ummesafi S, Iqbal A (2019) Content based image retrieval using image features information fusion. Inf Fusion 51:76–99
Ghosh N, Agrawal S, Motwani M (2018) A survey of feature extraction for content-based image retrieval system. In: Proceedings of international conference on recent advancement on computer and communication. Springer, Singapore
Liu G-H, Yang J-Y (2013) Content-based image retrieval using color difference histogram. Pattern Recogn 46(1):188–198
Sundara Vadivel P et al (2019) An efficient CBIR system based on color histogram, edge, and texture features. Concurrency Comput Pract Experience 31(12):e4994
Singh S, Batra S (2020) An efficient bi-layer content-based image retrieval system. Multimed Tools Appl 79
Yuan B-H, Liu G-H (2020) Image retrieval based on gradient-structures histogram. Neural Comput Appl 32(15):11717–11727
Alsmadi MK (2020) Content-based image retrieval using color, shape and texture descriptors and features. Arab J Sci Eng 45(4):3317–3330
Geetha V et al (2020) Efficient hybrid multi-level matching with diverse set of features for image retrieval. Soft Comput, 1–22
Chigateri MK, Sonoli S (2021) CBIR algorithm development using RGB histogram-based block contour method to improve the retrieval performance. Mater Today Proc (2021)
Martey EM et al (2021) Image representation using stacked color histogram. Algorithms 14(8):228
Li J, Wang JZ (2008) Real-time computerized annotation of pictures. IEEE Trans Pattern Anal Mach Intell 30(6):985–1002
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Varish, N., Singh, P., Yaser, S., Surapaneni, A., Reddy, B.V. (2022). Performance Analysis of Image Retrieval Method Using Quantized Bins of Color Histogram. In: Rout, R.R., Ghosh, S.K., Jana, P.K., Tripathy, A.K., Sahoo, J.P., Li, KC. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 427. Springer, Singapore. https://doi.org/10.1007/978-981-19-1018-0_51
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DOI: https://doi.org/10.1007/978-981-19-1018-0_51
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