An Improved Classification Model Based on Feature Fusion for Orchid Species

Orchid is a kind of terrestrial herb and it has elegant flower posture, quiet flower fragrance, rich colors and noble moral, therefore it has high ornamental value and is deeply loved by people. There are many kinds of orchids, and some of them are similar in shape, texture and color, which make people difficult to quickly and correctly distinguish them. As the existing classification model of orchid species have the problems of low accuracy rate and long classification time because of the inter species similarities and intra species differences in orchid species, thus influencing its wide application. In order to solve the problem above, in this paper, an improved classification model based on feature fusion is proposed for orchid species. The achievement of the paper lies in the fact that we successfully developed a classification model based on feature fusion to realize the high-efficient classification for orchid species. Specifically, in our scheme, firstly we obtained 12 orchid image sets with number of 12,227 images by network and field photography; Secondly we analyzed and studied the semantic relationship of different scale features from acquired orchid images above; Thirdly we designed an improved classification model based on feature fusion on the basis of the semantic relationship above; At last, we used the classification model above to realize the high-efficient classification for 12 orchid species. The experimental results showed that our proposed classification model based on feature fusion in this paper can realize 92.98% classification accuracy rate compared with classification models without using feature fusion technology, which can greatly improve the classification efficiency for orchid species.