Improving the Performance of Convolutional Neural Networks for Image Classification

As a high performance method for various image processing tasks, deep convolutional neural networks (CNNs) have reached impressive performances and absorbed considerable attention in the last few years. However, object classification on small size datasets for which a limited number of training images is available is still considered as an open problem. In this paper, we investigate a new method to effectively extract semantic image features. The proposed method which is based on CNNs boosts the performance of the object classification problem on small size dataset. To this end, a new method using image segmentation and CNNs is investigated. Our main goal is to increase the classification accuracy by first detecting and then extracting the main object of images. As training CNNs on small datasets does not yield to high performances because of millions of parameters to be learned, we propose using transfer learning strategy. Consequently, we first determine the main object of an image, and then we extract it. The extracted main object is used to tune the weights of the CNN in the training process. In this study, we employ a CNN that has been trained on the ImageNet dataset to reach mid-level image representation. Our experiments on Caltech-101 object dataset have shown that the proposed method substantially defeats other state-of-the-art methods.