Publication Type:

Book Chapter

Source:

Lecture Notes in Computational Vision and Biomechanics, Springer Netherlands, Volume 30, p.583-593 (2019)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060218376&doi=10.1007%2f978-3-030-00665-5_58&partnerID=40&md5=7bc7fe5c7fb95876b688f561977871ce

Abstract:

<p>Images consist of visual components such as color, shape, and texture. These components stand as the primary basis with which images are distinguished. A content-based image retrieval system extracts these primary features of an image and checks the similarity of the extracted features with those of the image given by the user. A group of images similar to the query image fed is obtained as a result. This paper proposes a new methodology for image retrieval using the local descriptors of an image in combination with one another. HSV histogram, Color moments, Color auto correlogram, Histogram of Oriented Gradients, and Wavelet transform are used to form the feature descriptor. In this work, it is found that a combination of all these features produces promising results that supersede previous research. Supervised learning algorithm, SVM is used for classification of the images. Wang dataset is used to evaluate the proposed system. © Springer Nature Switzerland AG 2019.</p>

Notes:

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Cite this Research Publication

B. Akshaya, S. Sri, S., A. Sathish, N., Shobika, K., Karthika, R., and Dr. Latha Parameswaran, “Content-based image retrieval using hybrid feature extraction techniques”, in Lecture Notes in Computational Vision and Biomechanics, vol. 30, Springer Netherlands, 2019, pp. 583-593.