Publications

In this paper we propose a large-scale Image annotation system for the Scalable Concept Image Annotation task. For each concept to be detected a separated classifier is built using the provided textual annotation. Images are represented as a Multivariate Gaussian distribution of a set of local features extracted over a dense regular grid. Textual analysis, on the web pages containing training images, is performed to retrieve a relevant set of samples for learning each concept classifier. An online SVMs solver based on Stochastic Gradient Descent is used to manage the large amount of training data. Experimental results show that the combination of different kind of local features encoded with our strategy achieves very competitive performance both in terms of mAP and mean F-measure.

This paper describe the ViSOR (Video Surveillance Online Repository) repository, designed with the aim of establishing an open platform for collecting, annotating, retrieving, and sharing surveillance videos, as well as evaluating the performance of automatic surveillance systems. The repository is free and researchers can collaborate sharing their own videos or datasets. Most of the included videos are annotated. Annotations are based on a reference ontology which has been defined integrating hundreds of concepts, some of them coming from the LSCOM and MediaMill ontologies. A new annotation classification schema is also provided, which is aimed at identifying the spatial, temporal and domain detail level used. The web interface allows video browsing, querying by annotated concepts or by keywords, compressed video previewing, media downloading and uploading. Finally, ViSOR includes a performance evaluation desk which can be used to compare different annotations.

In this paper, we suggest an interaction model designed to fit users' expectations in front of an image retrieval system. A lightweight relevance feedback strategy, working directly on the 2D projection of image features, allows the user to spatially navigate the media collection maintaining the real-time constraint. A preliminary evaluation of this relevance feedback strategy shows good performance compared with other known approaches.

For this demo, we present a set of stark applications designed to evaluate the performance of a color similarity retrieval system against human operators performance in the same tasks. The proposed series of tests give some interesting insights about the perception of color classes and the reliability of manual annotation in the fashion context. © 2012 Springer-Verlag.

Chimeric phenomena have been recently recognized to play a significant role in the investigation and understanding of the fundamental mechanisms behind highly diffused pathologies such as tumors. In this paper we present a new methodology for the detection of fusion transcript from Next Generation Sequencing (NGS) data. The methodology exploits short paired-end reads coming from RNA-Seq experiments to determine a list of fused genes and to exactly identify the fusion boundaries, so that the exact chimeric sequence can be analysed. Both known and unknown transcripts are considered, enabling the detection of fusions involving unannotated genes. An automated toolflow that reports a set of candidate fused genes and the associated junctions has been implemented and applied to a publicly available data set of melanoma.

Analysis of images obtained from two-dimensional gel electrophoresis (2D-GE) is a topic of utmost importance in bioinformatics research, since commercial and academic software available currently has proven to be neither completely effective nor fully automatic, often requiring manual revision and refinement of computer generated matches. In this work, we present an effective technique for the detection and the reconstruction of over-saturated protein spots. Firstly, the algorithm reveals overexposed areas, where spots may be truncated, and plateau regions caused by smeared and overlapping spots. Next, it reconstructs the correct distribution of pixel values in these overexposed areas and plateau regions, using a two-dimensional least-squares fitting based on a generalized Gaussian distribution. Pixel correction in saturated and smeared spots allows more accurate quantification, providing more reliable image analysis results. The method is validated for processing highly exposed 2D-GE images, comparing reconstructed spots with the corresponding non-saturated image, demonstrating that the algorithm enables correct spot quantification.

Analysis of 2D-GE images is a hot topic in bioinformatics research, since currently available commercial and academic software has proven to be not really effective and not completely automatic, often requiring manual revision of spots detection and refinement of computer generated matches. In this work, we present an effective technique for the detection and the reconstruction of over-saturated protein spots. Firstly, it reveals overexposed areas where spots may be truncated, and plateau regions caused by smeared and overlapped spots. As next, the correct distribution of pixel values in the overexposed areas and plateau regions is recovered by a two-dimensional fitting based on a generalized Gaussian distribution approximating the spots volume. Pixel correction according to the generalized Gaussian curve in saturated and smeared spots allows more accurate quantifications, providing more reliable image analysis results. As validation, we process highly exposed 2D-GE image, containing saturate spots, with respect to the corresponding non-saturated image, confirming that the method can effectively fix the saturated spots and enable correct spots quantification.

The discovery by Morris et al. (1994) of the genes contributing to the t(2;5)(p23;q35) translocation has laid the foundation for a molecular based recognition of anaplastic large cell lymphoma and highlighted the need for a further stratification of T-cell neoplasia. Likewise the detection of anaplastic lymphoma kinase (ALK) genetic lesions among many human cancers has defined unique subsets of cancer patients, providing new opportunities for innovative therapeutic interventions. The objective of this review is to appraise the molecular mechanisms driving ALK-mediated transformation, and to maintain the neoplastic phenotype. The understanding of these events will allow the design and implementation of novel tailored strategies for a well-defined subset of cancer patients.

The analysis of anti-nuclear antibodies in HEp-2 cells by indirect immunofluorescence (IIF) is fundamental for the diagnosis of important immune pathologies; in particular, classifying the staining pattern of the cell is critical for the differential diagnosis of several types of diseases. Current tests based on human evaluation are time-consuming and suffer from very high variability, which impacts on the reliability of the results. As a solution to this problem, in this work we propose a technique that performs automated classification of the staining pattern. Our method combines textural feature extraction and a two-step feature selection scheme to select a limited number of image attributes that are best suited to the classification purpose and then recognizes the staining pattern by means of a Support Vector Machine module. Experiments on IIF images showed that our method is able to identify staining patterns with average accuracy of about 87%.