Part-based convolutional neural network for visual recognition
Mid-level element based representations have been proven to be very effective for visual recognition. We present a method to discover discriminative elements based on deep Convolutional Neural Networks (CNNs), namely Part-based CNN (P-CNN), which acts as the role of encoding module in part-based representation. The P-CNN can be attached at arbitrary layer of a pre-trained CNN and be trained using image-level labels. The training of P-CNN essentially corresponds to the optimization and selection of discriminative mid-level visual elements. For an input image, the output of P-CNN is naturally the part-based coding and can be directly used for image recognition. By applying P-CNN to multiple layers of a pretrained CNN, more diverse visual elements can be obtained for visual recognitions. Experiments are conducted on two recognition tasks and their results demonstrate the effectiveness of the proposed method.