Visual object recognition using multi-scale local binary patterns and line segment feature Online publication date: Wed, 11-Jul-2012
by Chao Zhu; Huanzhang Fu; Charles-Edmond Bichot; Emmanuel Dellandréa; Liming Chen
International Journal of Signal and Imaging Systems Engineering (IJSISE), Vol. 5, No. 2, 2012
Abstract: This paper presents two visual features for object recognition. One is multi-scale Local Binary Pattern (LBP) operator extracted from coarse-to-fine image blocks to well describe texture structures. The other is line segment feature based on Gestalt-inspired region segmentation and fast Hough transform to capture accurate geometric information. The experiments on the SIMPLIcity database and PASCAL VOC 2007 benchmark show the effectiveness of line segment feature, and significant accuracy improvement by using fine-level blocks for LBP. Moreover, fusing LBP from different block levels further boosts the performance and outperforms the state-of-the-art SIFT. Both features also prove complementary to SIFT.
Online publication date: Wed, 11-Jul-2012
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Signal and Imaging Systems Engineering (IJSISE):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org