Deep Feature Fusion Method for Images Classification

In this paper, a new method for extracting features from images is proposed based on fusion technology to recognize and classify image contents. To obtain the best features, the most important techniques used for extraction were combined, which include convolutional neural network (CNN), histogram of oriented gradient (HOG), and local binary pattern (LBP). This technique enables the model to fit features from the following aspects: integrating the features of shape, texture, scale, rotation, and translation. Homogeneous descriptors were also employed to feed the classification process, relying on a support vector machine (SVM) classifier. We applied this method to the oxford_flowers102 data set, due to the importance of flowers in the food chain of living organisms. Through experiments, we obtained results showing the superiority of the proposed method in terms of performance and accuracy over competing methods.