Exploiting supervised learning for finetuning deep CNNs in content based image retrieval
In this paper a novel CNN-based approach in the Content Based Image Retrieval domain that exploits supervised learning is proposed. We employ a deep CNN model to derive feature representations from the activations of the deepest layers and we refine the weights of the utilized layers in order to produce better image descriptors using information obtained from the available data labels. To this end, we adapt the pretrained model and we retrain it on the dataset so that each image representation comes closer in terms of Euclidean distance to its nearest relevant representations and moves away from the irrelevant ones. Experimental results on four publicly available datasets for image retrieval denote the effectiveness of the proposed method in enhancing the retrieval performance, outperforming other CNN-based retrieval techniques in three out of four datasets, as well as traditional handcrafted approaches.