title: Histogram of Oriented Gradient Based Gist Feature for Building Recognition.
abstract: We proposed a new method of gist feature extraction for building recognition and named the feature extracted by this method as the histogram of oriented gradient based gist (HOG-gist). The proposed method individually computes the normalized histograms of multiorientation gradients for the same image with four different scales. The traditional approach uses the Gabor filters with four angles and four different scales to extract orientation gist feature vectors from an image. Our method, in contrast, uses the normalized histogram of oriented gradient as orientation gist feature vectors of the same image. These HOG-based orientation gist vectors, combined with intensity and color gist feature vectors, are the proposed HOG-gist vectors. In general, the HOG-gist contains four multiorientation histograms (four orientation gist feature vectors), and its texture description ability is stronger than that of the traditional gist using Gabor filters with four angles. Experimental results using Sheffield Buildings Database verify the feasibility and effectiveness of the proposed HOG-gist.
authors.affiliation: School of Information Engineering, Northeast Electric Power University, Jilin 132012, China.
authors.forename: Bin
authors.identifier: 0000-0001-8268-0430
authors.initials: B
authors.lastname: Li
authors.affiliation: School of Information Engineering, Northeast Electric Power University, Jilin 132012, China.
authors.forename: Kaili
authors.initials: K
authors.lastname: Cheng
authors.affiliation: School of Computer Science and Technology, Jilin University, Changchun 130012, China.
authors.forename: Zhezhou
authors.initials: Z
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted; D000072458:Orientation, Spatial; D010363:Pattern Recognition, Automated; D060388:Support Vector Machine