Deep convolutional image retrieval: A general framework

In this paper a Convolutional Neural Network framework for Content Based Image Retrieval is proposed. We employ a deep CNN model to obtain the feature representations from the activations of the deepest layers and we retrain the network in order to produce more efficient image descriptors, 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 dataset are available, and the Relevance Feedback based Retraining, if feedback from users is available. We propose these approaches independently or in a pipeline, where each retraining approach operates as a pretraining step to the subsequent one. We also apply a query expansion method with spatial reranking on top of these approaches in order to boost the retrieval performance. The experimental evaluation on six 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.