Publication

Learning the structure of image collections with latent aspect models

Publications associées (162)

Variational methods for texture segmentation

In the last decades, image production has grown significantly. From digital photographs to the medical scans, including satellite images and video films, more and more data need to be processed. Consequently the number of applications based on digital imag ...

EPFL2009

Fast Texture Segmentation Based on Semi-local Region Descriptor and Active Contour

Jean-Philippe Thiran, Xavier Bresson, Nawal Houhou

In this paper, we present an efficient approach for unsupervised segmentation of natural and textural images based on the extraction of image features and a fast active contour segmentation model. We address the problem of textures where neither the gray-l ...

2009

Tagging and Retrieving Images with Co-Occurrence Models: from Corel to Flickr

Daniel Gatica-Perez, Nikhil Garg

This paper presents two models for content-based automatic image annotation and retrieval in web image repositories, based on the co-occurrence of tags and visual features in the images. In particular, we show how additional measures can be taken to addres ...

Idiap2009

Co-occurrence Models for Image Annotation and Retrieval

Nikhil Garg

We present two models for content-based automatic image annotation and retrieval in web image repositories, based on the co-occurrence of tags and visual features in the images. In particular, we show how additional measures can be taken to address the noi ...

Idiap2009

Contextual classification of image patches with latent aspect models

Daniel Gatica-Perez, Jean-Marc Odobez, Florent Monay Michaud, Pedro Manuel Da Silva Quelhas

We present a novel approach for contextual classification of image patches in complex visual scenes, based on the use of histograms of quantized features and probabilistic aspect models. Our approach uses context in two ways: (1) by using the fact that spe ...

2009

Utilisation de PLSI en recherche d'information

Jean-Cédric Chappelier, Emmanuel Eckard

The PLSI model (“Probabilistic Latent Semantic Indexing”) offers a document indexing scheme based on probabilistic latent category models. It entailed applications in diverse ﬁelds, notably in information retrieval (IR). Nevertheless, PLSI cannot process d ...

2009

Image Summaries using Database Saliency

Pascal Fua, Sabine Süsstrunk, Radhakrishna Achanta, Appu Shaji

It is useful to have a small set of representative images from a database of thousands of images to summarize its content. There are two key aspects of such image summaries: how to generate them and how to present them. We address both issues. We extend th ...

2009

Retrieving Ancient Maya Glyphs with Shape Context

Daniel Gatica-Perez, Jean-Marc Odobez, Edgar Roman Rangel

We introduce an interdisciplinary project for archaeological and computer vision research teams on the analysis of the ancient Maya writing system. Our first task is the automatic retrieval of Maya syllabic glyphs using the Shape Context descriptor. We inv ...

IEEE2009

D-finder: A tool for compositional deadlock detection and verification

Joseph Sifakis

D-Finder tool implements a compositional method for the verification of component-based systems described in BIP language encompassing multi-party interaction. For deadlock detection, D-Finder applies proof strategies to eliminate potential deadlocks by co ...

2009

A collaborative approach to image segmentation and behavior recognition from image sequences

Laura Ioana Gui

Visual behavior recognition is currently a highly active research area. This is due both to the scientific challenge posed by the complexity of the task, and to the growing interest in its applications, such as automated visual surveillance, human-computer ...

EPFL2008

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Connectez-vous pour utiliser Chat avec Graph Search