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.

The performance of an automatic system for extracting flower boundaries for ten different types of wild flowers has been improved. The proposed flower boundary extraction method consists of three major procedures: the detection of four edge points, boundary tracing and performance improvement part. The flower boundary extraction part involves a new technique for automatically identifying the boundary of a flower in an image. An Intelligent Scissor algorithm is applied for boundary tracing. The color gradient magnitude and Canny edge detection are analyzed and included as the cost terms of the Intelligent Scissor algorithm. The color gradient magnitude cost term is implemented so that it can act directly on the three components of the color image. For the third procedure, we implement performance improvement. The main advantage of the proposed method was that when the program detected the wrong four edge points, using the mouse the correct positions could be clicked. The proposed method was applied to 500 photos of 10 different flowers, with 50 photos of each flower all in a complex background. The experimental results showed an extraction rate of 79.4%, which was better than before.