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Improved Performance of Visual Concept Detection in Images Using Bagging Approach with Support Vector Machines

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Computer Vision and Image Processing (CVIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1148))

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With rapid advances in imaging devices and internet, millions of images are uploaded on the internet without much information about the image. An efficient method is necessary for detecting the concept of the desired image from this vast collection of images. In this paper, Support Vector Machine (SVM) based architecture is presented to detect concept of a given input image. To enhance the performance of proposed system, a bagging approach is implemented. Color moments, HSV Color Histogram, Grey level co-occurrence matrix, Wavelet Transform and Edge orientation histogram are used for image representation purpose. These low-level feature descriptors are used to train multiple SVM models. The final concept of the query image is obtained by voting from outputs of these multiple models. The proposed system is evaluated on Wang’s Corel 10K. Results of proposed system indicate its improved performance over existing systems.

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Correspondence to Sanjay M. Patil .

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Patil, S.M., Bhoyar, K.K. (2020). Improved Performance of Visual Concept Detection in Images Using Bagging Approach with Support Vector Machines. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1148. Springer, Singapore.

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  • Print ISBN: 978-981-15-4017-2

  • Online ISBN: 978-981-15-4018-9

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