Integrated SURF and Spatial Augmented Color Feature Based Bovw Model with Svm for Image Classification
Mareedu Soniya1, Pallikonda Sarah Suhasini2
1M.Soniya, Department of ECE, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India.
2Pallikonda Sarah Suhasini, Associate Professor, Department of ECE, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1875-1877 | Volume-8 Issue-6, August 2019. | Retrieval Number: F7900088619/2019©BEIESP | DOI: 10.35940/ijeat.F7900.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, Bag-of-visual-words (BoVW) model with Speed up robust features (SURF) and spatial augmented color features for image classification is proposed. In BOVW model image is designated as vector of features occurrence count. This model ignores spatial information amongst patches, and SURF Feature descriptor is relevant to gray images only. As spatial layout of the extracted feature is important and color is a vital feature for image recognition, in this paper local color layout feature is augmented with SURF feature. Feature space is quantized using K-means clustering for feature reduction in constructing visual vocabulary. Histogram of visual word occurrence is then obtained which is applied to multiclass SVM classifier. Experimental results show that accuracy is improved with the proposed method.
Keywords: Bag-of-visual-words (BoVW), Spatial augmented color features, K-means clustering, SVM.