University of Trento

About: University of Trento is a education organization based out in Trento, Italy. It is known for research contribution in the topics: Population & Context (language use). The organization has 10527 authors who have published 30978 publications receiving 896614 citations. The organization is also known as: Universitá degli Studi di Trento & Universita degli Studi di Trento.

Semisupervised Feature Selection via Spline Regression for Video Semantic Recognition

Abstract: To improve both the efficiency and accuracy of video semantic recognition, we can perform feature selection on the extracted video features to select a subset of features from the high-dimensional feature set for a compact and accurate video data representation. Provided the number of labeled videos is small, supervised feature selection could fail to identify the relevant features that are discriminative to target classes. In many applications, abundant unlabeled videos are easily accessible. This motivates us to develop semisupervised feature selection algorithms to better identify the relevant video features, which are discriminative to target classes by effectively exploiting the information underlying the huge amount of unlabeled video data. In this paper, we propose a framework of video semantic recognition by semisupervised feature selection via spline regression $({\rm S}^{2}{\rm FS}^{2}{\rm R})$ . Two scatter matrices are combined to capture both the discriminative information and the local geometry structure of labeled and unlabeled training videos: A within-class scatter matrix encoding discriminative information of labeled training videos and a spline scatter output from a local spline regression encoding data distribution. An $\ell_{2,1}$ -norm is imposed as a regularization term on the transformation matrix to ensure it is sparse in rows, making it particularly suitable for feature selection. To efficiently solve ${\rm S}^{2}{\rm FS}^{2}{\rm R}$ , we develop an iterative algorithm and prove its convergency. In the experiments, three typical tasks of video semantic recognition, such as video concept detection, video classification, and human action recognition, are used to demonstrate that the proposed ${\rm S}^{2}{\rm FS}^{2}{\rm R}$ achieves better performance compared with the state-of-the-art methods.

Advances in Microclimate Ecology Arising from Remote Sensing

Abstract: Microclimates at the land-air interface affect the physiological functioning of organisms which, in turn, influences the structure, composition, and functioning of ecosystems. We review how remote sensing technologies that deliver detailed data about the structure and thermal composition of environments are improving the assessment of microclimate over space and time. Mapping landscape-level heterogeneity of microclimate advances our ability to study how organisms respond to climate variation, which has important implications for understanding climate-change impacts on biodiversity and ecosystems. Interpolating in situ microclimate measurements and downscaling macroclimate provides an organism-centered perspective for studying climate-species interactions and species distribution dynamics. We envisage that mapping of microclimate will soon become commonplace, enabling more reliable predictions of species and ecosystem responses to global change.

Prompt and non-prompt J/ψ production in pp collisions at √s = 7 TeV

Abstract: The production of J/psi mesons is studied in pp collisions at sqrt(s)=7 TeV with the CMS experiment at the LHC. The measurement is based on a dimuon sample corresponding to an integrated luminosity of 314 inverse nanobarns. The J/psi differential cross section is determined, as a function of the J/psi transverse momentum, in three rapidity ranges. A fit to the decay length distribution is used to separate the prompt from the non-prompt (b hadron to J/psi) component. Integrated over J/psi transverse momentum from 6.5 to 30 GeV/c and over rapidity in the range |y| < 2.4, the measured cross sections, times the dimuon decay branching fraction, are 70.9 \pm 2.1 (stat.) \pm 3.0 (syst.) \pm 7.8(luminosity) nb for prompt J/psi mesons assuming unpolarized production and 26.0 \pm 1.4 (stat.) \pm 1.6 (syst.) \pm 2.9 (luminosity) nb for J/psi mesons from b-hadron decays.

Detecting Cars in UAV Images With a Catalog-Based Approach

Abstract: This paper presents a new method for the automatic detection of cars in unmanned aerial vehicle (UAV) images acquired over urban contexts. UAV images are characterized by an extremely high spatial resolution, which makes the detection of cars particularly challenging. The proposed method starts with a screening operation in which the asphalted areas are identified in order to make the car detection process faster and more robust. Subsequently, filtering operations in the horizontal and vertical directions are performed to extract histogram-of-gradient features and to yield a preliminary detection of cars after the computation of a similarity measure with a catalog of cars used as reference. Three different strategies for computing the similarity are investigated. Successively, for the image points identified as potential cars, an orientation value is computed by searching for the highest similarity value in 36 possible directions. The last step is devoted to the merging of the points which belong to the same car because it is likely that a car is identified by more than one point due to the extremely high resolution of UAV images. As outcomes, the proposed method provides the number of cars in the image, as well as the position and orientation for each of them. Interesting experimental results, conducted on a set of real UAV images acquired over an urban area, are presented and discussed.

A Survey on Learning to Hash

Abstract: Nearest neighbor search is a problem of finding the data points from the database such that the distances from them to the query point are the smallest. Learning to hash is one of the major solutions to this problem and has been widely studied recently. In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations. We separate quantization from pairwise similarity preserving as the objective function is very different though quantization, as we show, can be derived from preserving the pairwise similarities. In addition, we present the evaluation protocols, and the general performance analysis, and point out that the quantization algorithms perform superiorly in terms of search accuracy, search time cost, and space cost. Finally, we introduce a few emerging topics.