We present a generic unsupervised method to increase the discriminative power of image
vectors obtained from a broad family of deep neural networks for object retrieval. This goal is …

In this paper, we address the problem of image retrieval by learning images representation
based on the activations of a Convolutional Neural Network. We present an end-to-end …

The focus of our work is speeding up evaluation of deep neural networks in retrieval
scenarios, where conventional architectures may spend too much time on negative …

Recently, image representation built upon Convolutional Neural Network (CNN) has been
shown to provide effective descriptors for image search, outperforming pre-CNN features as …

Deep convolutional neural networks have been successfully applied to image classification
tasks. When these same networks have been applied to image retrieval, the assumption has …

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 …

Learning feature representations for image retrieval is essential to multimedia search and
mining applications. Recently, deep convolutional networks (CNNs) have gained much …

Several recent approaches showed how the representations learned by Convolutional
Neural Networks can be repurposed for novel tasks. Most commonly it has been shown that …

Performance of convolutional neural network based image retrieval depends on the
characteristics and statistics of the data being used for training. We show that for training …

Image retrieval is a challenging problem that requires learning generalized features enough
to identify untrained classes, even with very few classwise training samples. In this article, to …