IMAGE CLASSIFICATION METHOD WITH MULTI-SCALE FEATURES

The study of complex image classification involves extracting features of different scales of a rnixed-grained data set. However, the output feature of convolutional neural network is fixed and cannot fully, satisfy the classification requirements of the image. This problem has a certain impact on feature extraction. In this paper, we propose a new multi-scale model based on Multi Scale feature with Support Vector Machine (MS-SVM) algorithm for adaptive and robust complex objects classification, also we aggregate localization features and high-level semantics features for this purpose. A multi-scale feature space is established for weighting the extracted multi-scale features. The experimental results show that the proposed method was able to achieve accuracies of 97.2% on the MalayaKew (MK) Leaf Dataset, and has a strong generalization in the common datasets.