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- Title
Sub Oriented Histograms of Local Binary Patterns for Smoke Detection and Texture Classification.
- Authors
Feiniu Yuan; Jinting Shi; Xue Xia; Yong Yang; Yuming Fang; Rui Wang
- Abstract
Local Binary Pattern (LBP) and its variants have powerful discriminative capabilities but most of them just consider each LBP code independently. In this paper, we propose sub oriented histograms of LBP for smoke detection and image classification. We first extract LBP codes from an image, compute the gradient of LBP codes, and then calculate sub oriented histograms to capture spatial relations of LBP codes. Since an LBP code is just a label without any numerical meaning, we use Hamming distance to estimate the gradient of LBP codes instead of Euclidean distance. We propose to use two coordinates systems to compute two orientations, which are quantized into discrete bins. For each pair of the two discrete orientations, we generate a sub LBP code map from the original LBP code map, and compute sub oriented histograms for all sub LBP code maps. Finally, all the sub oriented histograms are concatenated together to form a robust feature vector, which is input into SVM for training and classifying. Experiments show that our approach not only has better performance than existing methods in smoke detection, but also has good performance in texture classification.
- Subjects
DIGITAL image processing; EUCLIDEAN distance; TEXTURE analysis (Image processing); IMAGE analysis; HAMMING distance
- Publication
KSII Transactions on Internet & Information Systems, 2016, Vol 10, Issue 4, p1807
- ISSN
1976-7277
- Publication type
Article
- DOI
10.3837/tiis.2016.04.019