AbstractsComputer Science

Flower Image Classification Based on the Support Vector Machine

by Tsai-ming Sun

Institution: NSYSU
Year: 2016
Keywords: HSV color space; support vector machine; flower images; speed up robust feature; histogram of gradient; local binary pattern; local ternary pattern
Posted: 02/05/2017
Record ID: 2063854
Full text PDF: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0521116-192334


This thesis proposes a method of flower image classification, based on the support vector machine (SVM). There are three main stages in our method: (1) Perform segmentation on the input flower image and remove the background. (2) Extract several feature sets from the image. (3) Train the classification model by SVM with various combinations of feature sets. The feature sets include color features and texture features. The experimental dataset is the 102-category flower dataset [14]. As the experimental results show, the best accuracy of our method is 67.66%. However, with the same dataset, Nilsback and Zisserman's method [15] achieved the best accuracy 72.8%. We guess that the experimental environment leads to difference. Advisors/Committee Members: Chang-Biau Yang (committee member), Yung-Hsing Peng (chair), Chiou-Yi Hor (chair), Ngai-Ching Wong (chair).