Marco Bressan

Adapted vocabularies for generic visual categorization

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 obtained through the adaptation of the universal vocabulary using class-specific data. An image is characterized by a set of histograms – one per class – where each histogram describes whether the image content is best modeled by the universal vocabulary or the corresponding class vocabulary. It is shown experimentally on three very different databases that this novel representation outperforms those approaches which characterize an image with a single histogram.

Tone-mapping high dynamic range images by novel histogram adjustment

In this paper, we present novel histogram adjustment methods for displaying high dynamic range image. We first present a global histogram adjustment based tone mapping operator, which well reproduces global contrast for high dynamic range images. We then segment images and carry out adaptive contrast adjustment using our global tone mapping operator in the local regions to reproduce local contrast and ensure better quality. We demonstrate that our methods are fast, easy to use and a fixed set of parameter values produce good results for a wide variety of images.

Crossing textual and visual content in different application scenarios

This paper deals with multimedia information access. We propose two new approaches for hybrid text-image information processing that can be straightforwardly generalized to the more general multimodal scenario. Both approaches fall in the trans-media pseudo-relevance feedback category. Our first method proposes using a mixture model of the aggregate components, considering them as a single relevance concept. In our second approach, we define trans-media similarities as an aggregation of monomodal similarities between the elements of the aggregate and the new multimodal object. We also introduce the monomodal similarity measures for text and images that serve as basic components for both proposed trans-media similarities. We show how one can frame a large variety of problem in order to address them with the proposed techniques: image annotation or captioning, text illustration and multimedia retrieval and clustering. Finally, we present how these methods can be integrated in two applications: a travel blog assistant system and a tool for browsing the Wikipedia taking into account the multimedia nature of its content.

Local contrast enhancement

We introduce a novel algorithm for local contrast enhancement. The algorithm exploits a background image which is estimated with an edge-preserving filter. The background image controls a gain which enhances important details hidden in underexposed regions of the input image. Our designs for the gain, edge-preserving filter and chrominance recovery avoid artifacts and ensure the superior image quality of our results, as extensively validated by user evaluations. Unlike previous local contrast methods, ours is fully automatic in the sense that it can be directly applied to any input image with no parameter adjustment. This is because we exploit a trainable decision mechanism which classifies images as benefiting from enhancement or otherwise. Finally, a novel windowed TRC mechanism based on monotonic regression ensures that the algorithm takes only 0.3 s to process a 10 MPix image on a 3 GHz Pentium.