Flower Image Recognition Using Multi-Class SVM

Abstract:

This In this paper, a specific system is developed to recognize images of flower types. The proposed automatic flower boundary extraction method consists of two major procedures: the detection of four edge points and boundary tracing. Flower recognition includes two stages: feature extraction and matching. For the flower boundary extraction portion, we present a new technique for automatically identifying a flower’s boundary in an image. For boundary tracing, an intelligent scissors algorithm is applied. The color gradient magnitude cost term is implemented so that it can act directly on the three components of the color image. Suggested extraction of the characteristics has used division of the image in three levels (level 1, level 2, and level 3), the RGB and YCbCr of each level, the minimum Euclidean distance value of eight colors, and the number of petals. Using multi-class SVM, this dissertation derived 97.07% recognition of thirteen different types of flower images.

• Periodical: Applied Mechanics and Materials (Volumes 284-287)
• Pages: 3106-3110
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.284-287.3106
• Online since: January 2013
• Authors: Hyo Haeng Lee, Xiang Hua Li, Kwang Woo Chung, Kwang Seok Hong
• Keywords: Automatic Boundary Extraction, Feature Extraction, Flower Recognition, Multi-Class SVM

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