Deep convolutional learning for Content Based Image Retrieval

In this paper we propose a model retraining method for learning more efficient convolutional representations for Content Based Image Retrieval. We employ a deep CNN model to obtain the feature representations from the activations of the convolutional layers using max-pooling, and subsequently we adapt and retrain the network, in order to produce more efficient compact image descriptors, which improve both the retrieval performance and the memory requirements, relying on the available information. Our method suggests three basic model retraining approaches. That is, the Fully Unsupervised Retraining, if no information except from the dataset itself is available, the Retraining with Relevance Information, if the labels of the training dataset are available, and the Relevance Feedback based Retraining, if feedback from users is available. The experimental evaluation on three publicly available image retrieval datasets indicates the effectiveness of the proposed method in learning more efficient representations for the retrieval task, outperforming other CNN-based retrieval techniques, as well as conventional hand-crafted feature-based approaches in all the used datasets. (C) 2017 Elsevier B.V. All rights reserved.