@InProceedings{6952501,
  author = {J. Xu and N. Jiang and S. Goto},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Non-Redundant Gradient Semantic Local Binary Patterns for pedestrian detection},
  year = {2014},
  pages = {1407-1411},
  abstract = {In this paper, a feature named Non-Redundant Gradient Semantic Local Binary Patterns (NRGSLBP) is proposed for pedestrian detection as a modified version of conventional Semantic Local Binary Patterns (SLBP). Calculations of this feature are carried out for both intensity and gradient magnitude image so that texture and gradient information are combined. Moreover, non-redundant patterns are adopted on SLBP for the first time, allowing better discrimination. Compared with SLBP, no additional cost of the feature dimensions NRGSLBP is necessary and the calculation complexity is considerably smaller than that of other features. Experimental results on several datasets show that the detection rate of our proposed feature outperforms those of other features such as Histogram of Orientated Gradient (HOG), Histogram of Templates (HOT), Bidirectional Local Template Patterns (BLTP), Gradient Local Binary Patterns (GLBP), SLBP and Covariance matrix (COV).},
  keywords = {feature extraction;image texture;object detection;pedestrian detection;nonredundant gradient semantic local binary patterns;NRGSLBP;gradient magnitude image;gradient information;texture information;histogram-of-orientated gradient;HOG;histogram-of-templates;HOT;bidirectional local template patterns;BLTP;gradient local binary patterns;GLBP;covariance matrix;COV;feature extraction;Feature extraction;Histograms;Training;Support vector machines;Semantics;Kernel;Computer vision;Pedestrian detection;feature extraction;non-redundant gradient semantic local binary patterns},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569923901.pdf},
}

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Calculations of this feature are carried out for both intensity and gradient magnitude image so that texture and gradient information are combined. Moreover, non-redundant patterns are adopted on SLBP for the first time, allowing better discrimination. Compared with SLBP, no additional cost of the feature dimensions NRGSLBP is necessary and the calculation complexity is considerably smaller than that of other features. Experimental results on several datasets show that the detection rate of our proposed feature outperforms those of other features such as Histogram of Orientated Gradient (HOG), Histogram of Templates (HOT), Bidirectional Local Template Patterns (BLTP), Gradient Local Binary Patterns (GLBP), SLBP and Covariance matrix (COV).","keywords":"feature extraction;image texture;object detection;pedestrian detection;nonredundant gradient semantic local binary patterns;NRGSLBP;gradient magnitude image;gradient information;texture information;histogram-of-orientated gradient;HOG;histogram-of-templates;HOT;bidirectional local template patterns;BLTP;gradient local binary patterns;GLBP;covariance matrix;COV;feature extraction;Feature extraction;Histograms;Training;Support vector machines;Semantics;Kernel;Computer vision;Pedestrian detection;feature extraction;non-redundant gradient semantic local binary patterns","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569923901.pdf","bibtex":"@InProceedings{6952501,\n author = {J. Xu and N. Jiang and S. Goto},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {Non-Redundant Gradient Semantic Local Binary Patterns for pedestrian detection},\n year = {2014},\n pages = {1407-1411},\n abstract = {In this paper, a feature named Non-Redundant Gradient Semantic Local Binary Patterns (NRGSLBP) is proposed for pedestrian detection as a modified version of conventional Semantic Local Binary Patterns (SLBP). Calculations of this feature are carried out for both intensity and gradient magnitude image so that texture and gradient information are combined. Moreover, non-redundant patterns are adopted on SLBP for the first time, allowing better discrimination. Compared with SLBP, no additional cost of the feature dimensions NRGSLBP is necessary and the calculation complexity is considerably smaller than that of other features. Experimental results on several datasets show that the detection rate of our proposed feature outperforms those of other features such as Histogram of Orientated Gradient (HOG), Histogram of Templates (HOT), Bidirectional Local Template Patterns (BLTP), Gradient Local Binary Patterns (GLBP), SLBP and Covariance matrix (COV).},\n keywords = {feature extraction;image texture;object detection;pedestrian detection;nonredundant gradient semantic local binary patterns;NRGSLBP;gradient magnitude image;gradient information;texture information;histogram-of-orientated gradient;HOG;histogram-of-templates;HOT;bidirectional local template patterns;BLTP;gradient local binary patterns;GLBP;covariance matrix;COV;feature extraction;Feature extraction;Histograms;Training;Support vector machines;Semantics;Kernel;Computer vision;Pedestrian detection;feature extraction;non-redundant gradient semantic local binary patterns},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569923901.pdf},\n}\n\n","author_short":["Xu, J.","Jiang, N.","Goto, S."],"key":"6952501","id":"6952501","bibbaseid":"xu-jiang-goto-nonredundantgradientsemanticlocalbinarypatternsforpedestriandetection-2014","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569923901.pdf"},"keyword":["feature extraction;image texture;object detection;pedestrian detection;nonredundant gradient semantic local binary patterns;NRGSLBP;gradient magnitude image;gradient information;texture information;histogram-of-orientated gradient;HOG;histogram-of-templates;HOT;bidirectional local template patterns;BLTP;gradient local binary patterns;GLBP;covariance matrix;COV;feature extraction;Feature extraction;Histograms;Training;Support vector machines;Semantics;Kernel;Computer vision;Pedestrian detection;feature extraction;non-redundant gradient semantic local binary patterns"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2014url.bib","creationDate":"2021-02-13T17:43:41.690Z","downloads":0,"keywords":["feature extraction;image texture;object detection;pedestrian detection;nonredundant gradient semantic local binary patterns;nrgslbp;gradient magnitude image;gradient information;texture information;histogram-of-orientated gradient;hog;histogram-of-templates;hot;bidirectional local template patterns;bltp;gradient local binary patterns;glbp;covariance matrix;cov;feature extraction;feature extraction;histograms;training;support vector machines;semantics;kernel;computer vision;pedestrian detection;feature extraction;non-redundant gradient semantic local binary patterns"],"search_terms":["non","redundant","gradient","semantic","local","binary","patterns","pedestrian","detection","xu","jiang","goto"],"title":"Non-Redundant Gradient Semantic Local Binary Patterns for pedestrian detection","year":2014,"dataSources":["A2ezyFL6GG6na7bbs","oZFG3eQZPXnykPgnE"]}