Efficient deep features selections and classification for flower species recognition

Abstract

Image-based automatic flower species classification is an important problem for the biologists who construct digital flower catalogs. A dozen of work about flower species recognition has been proposed so far based on traditional image processing routines. Nowadays, researchers apply the deep learning on various image-based object recognition tasks. In this paper, deep convolutional neural networks (DCNN) based hybrid method is applied to flower species classification. The proposed method initially employs a pre-trained DCNN model for feature extraction. To this end, two popular DCNN architectures namely, AlexNet and VGG16 models are adopted. For constructing efficient feature sets, the features from AlexNet and VGG16 models are then concatenated. Finally, a feature selection algorithm, the minimum Redundancy Maximum Relevance (mRMR) method, is used to select the more efficient features. A support vector machine (SVM) classifier with Radial Bases Function (RBF) kernel is employed to classify the flower species using the extracted features. Flower17 and Flower102 datasets which have a huge amount of category are used in the experimental works. Various experiments results show that we have achieved 96.39% and 95.70% accuracy performance for Flower17 and Flower102, respectively. The obtained results demonstrate the effectiveness of the proposed method, despite the relative simplicity of the approach. (C) 2019 Elsevier Ltd. All rights reserved.