Title: Visual object recognition using multi-scale local binary patterns and line segment feature

Authors: Chao Zhu; Huanzhang Fu; Charles-Edmond Bichot; Emmanuel Dellandréa; Liming Chen

Addresses: Université de Lyon, CNRS Ecole Centrale de Lyon LIRIS, UMR5205, F-69134, France ' Université de Lyon, CNRS Ecole Centrale de Lyon LIRIS, UMR5205, F-69134, France ' Université de Lyon, CNRS Ecole Centrale de Lyon LIRIS, UMR5205, F-69134, France ' Université de Lyon, CNRS Ecole Centrale de Lyon LIRIS, UMR5205, F-69134, France ' Université de Lyon, CNRS Ecole Centrale de Lyon LIRIS, UMR5205, F-69134, France

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.

Keywords: visual recognition object recognition; feature extraction; LBPs; local binary patterns; line segment; geometric information; SIFT; texture structures; region segmentation; fast Hough transform.

DOI: 10.1504/IJSISE.2012.047782

International Journal of Signal and Imaging Systems Engineering, 2012 Vol.5 No.2, pp.85 - 92

Received: 31 Aug 2011
Accepted: 12 Jan 2012
Published online: 31 Dec 2014 *

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