Feature extraction with triplet convolutional neural network for content-based image retrieval

Feature representations and similarity metric are important for content-based image retrieval (CBIR) tasks. Inspired by recent successes of convolutional neural network (CNN) with hierarchical features, in this paper, we apply a triplet convolutional neural network (Triplet-CNN) to learn features with the criterion of similarity metric. Furthermore, we propose some strategies to improve the performance of Triplet-CNN for CBIR tasks. In training stage, in order to avoid the problem of lacking data in training Triplet-CNN, we utilize dataset in similar domains to retrain the network. In feature extraction stage, since the rectified linear unit (ReLU) activation function drops all negative neurons, we extract the non-activation based features to preserve the information. And then we combine features from different layers to retrieve images. Our experimental results demonstrate that our method can improve the retrieval performance of CBIR tasks.