Latent mixture vocabularies for object categorization and segmentation

被引：22

作者：

Larlus, Diane ^{[1
]}

Jurie, Frederic ^{[1
]}

机构：

[1] INRIA Rhone Alpes, F-38334 Montbonnot St Martin, St Ismier, France

来源：

IMAGE AND VISION COMPUTING | 2009年 / 27卷 / 05期

关键词：

Object categorization; Object segmentation; Visual vocabulary creation; IMAGE;

D O I：

10.1016/j.imavis.2008.04.022

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The visual vocabulary is an intermediate level representation which has been proved to be very powerful for addressing object categorization problems. It is generally built by vector quantizing a set of local image descriptors, independently of the object model used for categorizing images. We propose here to embed the visual vocabulary creation within the object model construction, allowing to make it more suited for object class discrimination and therefore for object categorization. We also show that the model can be adapted to perform object level segmentation task, without needing any shape model, making the approach very adapted to high intra-class varying objects. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：523 / 534

页数：12