Concept

Annotation automatique d'images

Publications associées (75)

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Evaluating Quality of Screen Content Images Via Structural Variation Analysis

Daniel Thalmann

With the quick development and popularity of computers, computer-generated signals have drastically invaded into our daily lives. Screen content image is a typical example, since it also includes graphic and textual images as components as compared with na ...

IEEE COMPUTER SOC2018

Large-Scale Image Segmentation with Convolutional Networks

Pedro Henrique Oliveira Pinheiro

Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...

EPFL2017

Large-Scale Image Segmentation with Convolutional Networks

Pedro Henrique Oliveira Pinheiro

Sciences et Techniques de l’Ingénieur (STI)2017

Adaptive relevance feedback for large-scale image retrieval

François Fleuret, Nicolae Suditu

Content-based image retrieval aims at substituting traditional indexing based on manual annotation by using automatically-extracted visual indexing features. Novel techniques are needed however to efficiently deal with the semantic gap (i.e. the partial ma ...

2016

Exploiting Large Image Sets for Road Scene Parsing

Mathieu Salzmann

There is an increasing interest in exploiting multiple images for scene understanding, with great progress in areas such as cosegmentation and video segmentation. Jointly analyzing the images in a large set offers the opportunity to exploit a greater sourc ...

Ieee-Inst Electrical Electronics Engineers Inc2016

On the Relevance of Sparsity for Image Classification

Pascal Fua, Engin Türetken, Vincent Lepetit, Germán González Serrano, Roberto Rigamonti, Matthew Brown, Fethallah Benmansour

In this paper we empirically analyze the importance of sparsifying representations for classification purposes. We focus on those obtained by convolving images with linear filters, which can be either hand designed or learned, and perform extensive experim ...

Elsevier2014

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

Analysis of image registration with tangent distance

Pascal Frossard, Elif Vural

The computation of the geometric transformation between a reference and a target image, known as image registration or alignment, corresponds to the projection of the target image onto the transformation manifold of the reference image (the set of images g ...

2014

Analysis of Image Registration with Tangent Distance

Pascal Frossard, Elif Vural

2014

Adaptive Sampling for Large Scale Boosting

François Fleuret, Charles Dubout

Classical Boosting algorithms, such as AdaBoost, build a strong classifier without concern for the computational cost. Some applications, in particular in computer vision, may involve millions of training examples and very large feature spaces. In such con ...

2014