toggle visibility Search & Display Options
Select All    Deselect All [1–10] << 11 12 13 14 >>
List View
 | 
Citations
 | 
Details
  Records Links
Author G. Lisanti; I. Masi; Andrew Bagdanov; Alberto del Bimbo edit  doi
openurl 
  Title Person Re-identification by Iterative Re-weighted Sparse Ranking Type Journal Article
  Year 2015 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 37 Issue 8 Pages 1629 - 1642  
  Keywords  
  Abstract In this paper we introduce a method for person re-identification based on discriminative, sparse basis expansions of targets in terms of a labeled gallery of known individuals. We propose an iterative extension to sparse discriminative classifiers capable of ranking many candidate targets. The approach makes use of soft- and hard- re-weighting to redistribute energy among the most relevant contributing elements and to ensure that the best candidates are ranked at each iteration. Our approach also leverages a novel visual descriptor which we show to be discriminative while remaining robust to pose and illumination variations. An extensive comparative evaluation is given demonstrating that our approach achieves state-of-the-art performance on single- and multi-shot person re-identification scenarios on the VIPeR, i-LIDS, ETHZ, and CAVIAR4REID datasets. The combination of our descriptor and iterative sparse basis expansion improves state-of-the-art rank-1 performance by six percentage points on VIPeR and by 20 on CAVIAR4REID compared to other methods with a single gallery image per person. With multiple gallery and probe images per person our approach improves by 17 percentage points the state-of-the-art on i-LIDS and by 72 on CAVIAR4REID at rank-1. The approach is also quite efficient, capable of single-shot person re-identification over galleries containing hundreds of individuals at about 30 re-identifications per second.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 0162-8828 ISBN Medium  
  Area Expedition Conference  
  Notes LAMP; 601.240; 600.079 Approved no  
  Call Number Admin @ si @ LMB2015 Serial 2557  
Permanent link to this record
 
 
Author Pedro Martins; Paulo Carvalho; Carlo Gatta edit   pdf
doi  openurl
  Title On the completeness of feature-driven maximally stable extremal regions Type Journal Article
  Year 2016 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 74 Issue Pages 9-16  
  Keywords Local features; Completeness; Maximally Stable Extremal Regions  
  Abstract By definition, local image features provide a compact representation of the image in which most of the image information is preserved. This capability offered by local features has been overlooked, despite being relevant in many application scenarios. In this paper, we analyze and discuss the performance of feature-driven Maximally Stable Extremal Regions (MSER) in terms of the coverage of informative image parts (completeness). This type of features results from an MSER extraction on saliency maps in which features related to objects boundaries or even symmetry axes are highlighted. These maps are intended to be suitable domains for MSER detection, allowing this detector to provide a better coverage of informative image parts. Our experimental results, which were based on a large-scale evaluation, show that feature-driven MSER have relatively high completeness values and provide more complete sets than a traditional MSER detection even when sets of similar cardinality are considered.  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier B.V. Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 0167-8655 ISBN Medium  
  Area Expedition Conference  
  Notes LAMP;MILAB; Approved no  
  Call Number Admin @ si @ MCG2016 Serial 2748  
Permanent link to this record
 
 
Author Adriana Romero; Carlo Gatta; Gustavo Camps-Valls edit   pdf
doi  openurl
  Title Unsupervised Deep Feature Extraction for Remote Sensing Image Classification Type Journal Article
  Year 2016 Publication IEEE Transaction on Geoscience and Remote Sensing Abbreviated Journal TGRS  
  Volume 54 Issue 3 Pages 1349 - 1362  
  Keywords  
  Abstract This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 0196-2892 ISBN Medium  
  Area Expedition Conference  
  Notes LAMP; 600.079;MILAB Approved no  
  Call Number Admin @ si @ RGC2016 Serial 2723  
Permanent link to this record
 
 
Author Fahad Shahbaz Khan; Shida Beigpour; Joost Van de Weijer; Michael Felsberg edit  doi
openurl 
  Title Painting-91: A Large Scale Database for Computational Painting Categorization Type Journal Article
  Year 2014 Publication Machine Vision and Applications Abbreviated Journal MVAP  
  Volume 25 Issue 6 Pages 1385-1397  
  Keywords  
  Abstract Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly, such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency estimation algorithms.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 0932-8092 ISBN Medium  
  Area Expedition Conference  
  Notes CIC; LAMP; 600.074; 600.079 Approved no  
  Call Number Admin @ si @ KBW2014 Serial 2510  
Permanent link to this record
 
 
Author Shida Beigpour; Christian Riess; Joost Van de Weijer; Elli Angelopoulou edit   pdf
doi  openurl
  Title Multi-Illuminant Estimation with Conditional Random Fields Type Journal Article
  Year 2014 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 23 Issue 1 Pages 83-95  
  Keywords color constancy; CRF; multi-illuminant  
  Abstract Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 1057-7149 ISBN Medium  
  Area Expedition Conference  
  Notes CIC; LAMP; 600.074; 600.079 Approved no  
  Call Number Admin @ si @ BRW2014 Serial 2451  
Permanent link to this record
Select All    Deselect All [1–10] << 11 12 13 14 >>
List View
 | 
Citations
 | 
Details
toggle visibility
Save Citations:
Export Records: