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Several state-of-the-art Generic Visual Categorization (GVC) systems are built around a vocabulary of visual terms and characterize images with one histogram of visual word counts. We propose a novel and practical approach to GVC based on a universal vocabulary , which describes the content of all the considered classes of images, and class vocabularies(More)
1 Motivation Real-life data sets are evolving over time: • new images or items are added every second • new labels, tags and products are incorporated over time Need to index, retrieve, search and categorize these items Therefore, we are interested in methods for large scale visual classification where we can add new images and new classes at near-zero cost(More)
We propose a simple approach to semantic image segmentation. Our system scores low-level patches according to their class relevance, propagates these posterior probabilities to pixels and uses low-level segmentation to guide the semantic segmentation. The two main contributions of this paper are as follows. First, for the patch scoring, we describe each(More)
We study large-scale image classification methods that can incorporate new classes and training images continuously over time at negligible cost. To this end, we consider two distance-based classifiers, the k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers, and introduce a new metric learning approach for the latter. We also introduce an(More)
In this paper, we automatically assess the aesthetic properties of images. In the past, this problem has been addressed by hand-crafting features which would correlate with best photographic practices (e.g. " Does this image respect the rule of thirds? ") or with photographic techniques (e.g. " Is this image a macro? "). We depart from this line of research(More)
We propose a novel framework for visual saliency detection based on a simple principle: images sharing their global visual appearances are likely to share similar salience. Assuming that an annotated image database is available, we first retrieve the most similar images to the target image; secondly, we build a simple classifier and we use it to generate(More)
This paper proposes a new approach for assessing the presence of a digital watermark in images and videos. This approach relies on a Bayesian formulation that allows to compute the probability that a watermark was generated using a given key. The watermarking itself relies on the discrete Fourier transform (DFT) of the image, of video frames or of three(More)