Open AccessFusing texture, edge and line features for smoke recognition
Author(s) -
Yuan Feiniu,
Li Gang,
Xia Xue,
Lei Bangjun,
Shi Jinting
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0012
Subject(s) - artificial intelligence , local binary patterns , pattern recognition (psychology) , hough transform , pixel , histogram , computer vision , computer science , canny edge detector , feature (linguistics) , line (geometry) , edge detection , feature extraction , boundary (topology) , feature vector , image texture , mathematics , image (mathematics) , image segmentation , image processing , geometry , mathematical analysis , linguistics , philosophy
To improve recognition accuracy, the authors fuse texture, edge and line information to propose a feature extraction method for smoke recognition. The Canny operator is proposed to generate an edge image from an original image, and then adopt the Hough transform to extract straight lines from the edge image. The lines are rasterised to generate a discrete line image and two local patterns are proposed for the edge and line images. The first one is local boundary summation pattern (LBSP) that computes the sum of binary pixel values along the boundary of a local region around a centre pixel. The second one is called local region summation pattern (LRSP) that sums up the binary values of pixels in a local region around the centre pixel. Besides LBSP and LRSP, LBPs with three mapping modes (LBP_M3) to achieve traditional texture information are also extracted. Finally, the authors concatenate the histograms of LBP_M3, LBSP and LRSP to generate a feature vector, and use support vector machine for classifying and testing. Experiments show that authors’ method outperforms most of existing traditional methods for smoke recognition. Although this method has low dimensional features, it also obtains good performance for multi‐class texture classification.