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Comic Digitization through the Extraction of Semantic Content and Style Analysis

Related publications (32)

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Sample Distillation for Object Detection and Image Classification

François Fleuret, Olivier Canévet, Leonidas Lefakis

We propose a novel approach to efficiently select informative samples for large-scale learning. Instead of directly feeding a learning algorithm with a very large amount of samples, as it is usually done to reach state-of-the-art performance, we have devel ...

2014

Object Classification and Detection in High Dimensional Feature Space

Charles Dubout

Object classification and detection aim at recognizing and localizing objects in real-world images. They are fundamental computer vision problems and a prerequisite for full scene understanding. Their difficulty lies in the large number of possible object ...

Programme doctoral en Informatique, Communications et Information2013

Anti-spoofing in action: joint operation with a verification system

Sébastien Marcel, André Anjos, Ivana Chingovska

Besides the recognition task, today's biometric systems need to cope with additional problem: spoofing attacks. Up to date, academic research considers spoofing as a binary classification problem: systems are trained to discriminate between real accesses a ...

2013

Learning to Detect Objects with Minimal Supervision

Karim Ali

Many classes of objects can now be successfully detected with statistical machine learning techniques. Faces, cars and pedestrians, have all been detected with low error rates by learning their appearance in a highly generic manner from extensive training ...

EPFL2012

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Recent years have seen an increasing interest in sparse representations for image classiﬁcation and object recognition, probably motivated by evidence from the analysis of the primate visual cortex. It is still unclear, however, whether or not sparsity hel ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Dynamic Quantification of Activity Recognition Capabilities in Opportunistic Systems

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Hesam Sagha

Opportunistic activity and context recognition systems draw from the characteristic to use sensing devices according to a recognition goal at runtime that just happen to be available instead of pre-defining them at the design time of the system. Whenever a ...

2011

Learning Pose Invariant and Covariant Classifiers from Image Sequences

Mustafa Özuysal

Object tracking and detection over a wide range of viewpoints is a long-standing problem in Computer Vision. Despite significant advance in wide-baseline sparse interest point matching and development of robust dense feature models, it remains a largely op ...

EPFL2010

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Recent years have seen an increasing interest in sparseness constraints for image classiﬁcation and object recognition, probably motivated by the evidence of sparse representations internal in the primate visual cortex. It is still unclear, however, whethe ...

2010

Fast Keypoint Recognition using Random Ferns

Pascal Fua, Vincent Lepetit, Michael Calonder, Mustafa Özuysal

While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in ...

2010

Semantic coding by supervised dimensionality reduction

Pascal Frossard, Effrosyni Kokiopoulou

This paper addresses the problem of representing multimedia information under a compressed form that permits efficient classification. The semantic coding problem starts from a subspace method where dimensionality reduction is formulated as a matrix factor ...

2008