Ensemble learning has demonstrated its efficiency in many computer vision tasks. In this paper, we address this paradigm within content based image retrieval (CBIR). We propose to build an ensemble of convolutional neural networks (CNNs), either by training the CNNs on different bags of images, or by using CNNs trained on the same dataset, but having different architectures. Each network is used to extract the class probability vectors from images to use them as representations. The final image representation is then generated by combining the extracted class probability vectors from the built ensemble. We show that the use of CNN ensembles is very efficient in generating a powerful image representation compared to individual CNNs. Moreover, we propose an Averarge Query Expansion technique for our proposal to enhance the retrieval results. Several experiments were conducted to extensively evaluate the application of ensemble learning in CBIR. Results in terms of precision, recall, and mean average precision show the outperformance of our proposal compared to the state of the art.

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In this paper, we present a Convolutional Neural Network (CNN) for feature extraction in Content Based Image Retrieval (CBIR). The proposed CNN aims at reducing the semantic gap between low-level and high-level features. Thus, improving retrieval results. Our CNN is the result of a transfer learning technique using Alexnet pretrained network. It learns how to extract representative features from a learning database and then uses this knowledge in query feature extraction. Experimentations performed on Wang (Corel 1K) database show a significant improvement in terms of precision over the state of the art classic approaches.

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