Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
Recognition invariance obtained by extended and invariant features
Neural Networks - 2004 Special issue Vision and brain
The equivalence of two-dimensional PCA to line-based PCA
Pattern Recognition Letters
Identifying Simple Discriminatory Gene Vectors with an Information Theory Approach
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Using natural class hierarchies in multi-class visual classification
Pattern Recognition
Sharing Visual Features for Multiclass and Multiview Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification in an informative sample subspace
Pattern Recognition
On the Design of Cascades of Boosted Ensembles for Face Detection
International Journal of Computer Vision
Learning to Locate Informative Features for Visual Identification
International Journal of Computer Vision
Efficient Learning of Relational Object Class Models
International Journal of Computer Vision
Generalizing the Lucas-Kanade algorithm for histogram-based tracking
Pattern Recognition Letters
Distinctive and compact features
Image and Vision Computing
Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection
International Journal of Computer Vision
Shape Based Detection and Top-Down Delineation Using Image Segments
International Journal of Computer Vision
CrowdReranking: exploring multiple search engines for visual search reranking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
DAVID: discriminant analysis for verification of monuments in image data
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Object detection using spatial histogram features
Image and Vision Computing
Learning to classify by ongoing feature selection
Image and Vision Computing
Information theoretic feature extraction for audio-visual speech recognition
IEEE Transactions on Signal Processing
Hierarchical appearance-based classifiers for qualitative spatial localization
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Part-based feature synthesis for human detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Weighted symbols-based edit distance for string-structured image classification
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multiclass object classification for real-time video surveillance systems
Pattern Recognition Letters
Images as sets of locally weighted features
Computer Vision and Image Understanding
Towards optimal training of cascaded detectors
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Face recognition using ordinal features
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Building detection from mobile imagery using informative SIFT descriptors
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Object recognition via local patch labelling
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
Attentive object detection using an information theoretic saliency measure
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
A generative model for multi class object recognition and detection
TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
A boundary-fragment-model for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Algorithm of feature estimation for real time objects detection in thermal images
CIMMACS'11/ISP'11 Proceedings of the 10th WSEAS international conference on Computational Intelligence, Man-Machine Systems and Cybernetics, and proceedings of the 10th WSEAS international conference on Information Security and Privacy
The Journal of Machine Learning Research
Probabilistic learning of similarity measures for tensor PCA
Pattern Recognition Letters
An information theoretic approach for feature selection
Security and Communication Networks
Hierarchical Classifiers for Robust Topological Robot Localization
Journal of Intelligent and Robotic Systems
Journal of Visual Communication and Image Representation
Bottom-up perceptual organization of images into object part hypotheses
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Beyond Independence: An Extension of the A Contrario Decision Procedure
International Journal of Computer Vision
Unified entity search in social media community
Proceedings of the 22nd international conference on World Wide Web
Mutual information-based method for selecting informative feature sets
Pattern Recognition
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In this paper we show that efficient object recognition canbeobtained by combining informative features with linearclassification. The results demonstrate the superiority ofinformative class-specific features, as compared with generic typefeatures such as wavelets, for the task of object recognition. Weshow that information rich features can reach optimal performancewith simple linear separation rules, while generic feature basedclassifiers require more complex classification schemes. This issignificant because efficient and optimal methods have beendeveloped for spaces that allow linear separation. To comparedifferent strategies for feature extraction, we trained andcompared classifiers working in feature spaces of the same lowdimensionality, using two feature types (image fragments vs.wavelets) and two classification rules (linear hyperplane and aBayesian Network). The results show that by maximizing theindividual information of the features, it is possible to obtainefficient classification by a simple linear separating rule, aswellas more efficient learning.