NDLI: Scene classification based on gray level-gradient co-occurrence matrix in the neighborhood of interest points

Content Provider	IEEE Xplore Digital Library
Author	Shuo Chen Chengdong Wu Dongyue Chen Wenjun Tan
Copyright Year	2009
Description	Author affiliation: College of Information Science & Engineering, Northeastern University, Shenyang, China (Shuo Chen; Chengdong Wu; Dongyue Chen; Wenjun Tan)
Abstract	Scene classification is an important application field of multimedia information technology, whereas how to extract features from image is one of the key technologies in scene classification and recognition. A new method of extracting features is presented in this paper, it extracts features through gray level-gradient co-occurrence matrix in the neighborhood of interest points, also it can reserve the key image edge information, and it is called GGNP for short in the paper. The weighted Gower's similarity coefficient model is adopted as the basis for image scene classification, as it is more flexible than Euclidean distance function. Compared with traditional methods, the method has a good invariance in image scaling, rotation, translation and robust across a substantial range of affine distortion, meanwhile having good real-time. Experimentations are designed to test the precision and time-consuming of the method, the results of experiments show that the method has good effects on scene classification.
Starting Page	482
Ending Page	485
File Size	118891
Page Count	4
File Format	PDF
ISBN	9781424447541
DOI	10.1109/ICICISYS.2009.5357627
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image recognition Content based retrieval Target recognition Image retrieval Interest points Gray level-gradient co-occurrence matrix Information retrieval Educational institutions Data mining Local feature vector Information science Scene classification Layout Feature extraction
Content Type	Text
Resource Type	Article

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