A Maximum Entropy Framework for Part-Based Texture and Object Recognition - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2005

A Maximum Entropy Framework for Part-Based Texture and Object Recognition

Abstract

This paper presents a probabilistic part-based approach for texture and object recognition. Textures are represented using a part dictionary found by quantizing the appearance of scale- or affine- invariant keypoints. Object classes are represented using a dictionary of composite semi-local parts, or groups of neighboring keypoints with stable and distinctive appearance and geometric layout. A discriminative maximum entropy framework is used to learn the posterior distribution of the class label given the occurrences of parts from the dictionary in the training set. Experiments on two texture and two object databases demonstrate the effectiveness of this framework for visual classification.
Fichier principal
Vignette du fichier
lana_iccv05.pdf (3.27 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

inria-00548510 , version 1 (20-12-2010)

Identifiers

Cite

Svetlana Lazebnik, Cordelia Schmid, Jean Ponce. A Maximum Entropy Framework for Part-Based Texture and Object Recognition. 10th International Conference on Computer Vision (ICCV '05), Oct 2005, Beijing, China. pp.832 - 838, ⟨10.1109/ICCV.2005.10⟩. ⟨inria-00548510⟩
226 View
470 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More