eprintid: 1785
rev_number: 7
eprint_status: archive
userid: 12
dir: disk0/00/00/17/85
datestamp: 2016-07-22 03:41:46
lastmod: 2016-07-22 03:41:46
status_changed: 2016-07-22 03:41:46
type: conference_item
metadata_visibility: show
creators_name: Nguyen Hong, Thinh
creators_name: Le Vu, Ha
creators_name: Barat, Cecile
creators_name: Ducottet, Christophe
creators_id: hongthinh.nguyen@vnu.edu.vn
creators_id: halv@vnu.edu.vn
creators_id: cecile.barat@univ-st-etienne.fr
creators_id: ducottet@univ-st-etienne.fr
title: String distance for automatic image classification
ispublished: pub
subjects: Communications
divisions: fac_fet
abstract: The Bag-of-visual Words (BOW) model has recently become the most popular representation to depict image content. It has proven to be quite effective for many multimedia and vision applications, especially for object recognition and scene classification or automatic image annotation. This model however ignores the spatial layout of features within images, which is yet discriminative for category classification. In this paper, we present a novel approach based on string matching to take into account geometric correspondences between images and facilitate category recognition. First, we propose to represent images as strings of histogram second, we introduce a new string distance in the context of image comparison. This distance automatically identifies local alignments between sub image regions and allows merging groups of similar sub-regions. Experiments on several dataset such as Scene-15, Caltech-101 and Pascal 2007 show that the proposed approach outperforms the classical BOW method and is competitive with state-of-the art techniques for image classification.
date: 2016-01-01
date_type: published
full_text_status: public
pres_type: paper
event_title: International Joint Conference on Convergence
event_location: Vietnam
event_type: conference
refereed: TRUE
citation:   Nguyen Hong, Thinh and Le Vu, Ha and Barat, Cecile and Ducottet, Christophe  (2016) String distance for automatic image classification.  In: International Joint Conference on Convergence, Vietnam.     
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/1785/1/IJCC2015_Thinh.pdf