NDLI: Zernike based scene categorization using artificial neural networks

Content Provider	IEEE Xplore Digital Library
Author	Devendran, V. Thiagarajan, H. Wahi, A.
Copyright Year	2008
Description	Author affiliation: Dept. of Math., Nat. Inst. of Technol., Trichy (Thiagarajan, H.) \|\| Dept. of Comput. Applic., Bannari Amman Inst. of Technol., Sathyamangalam (Devendran, V.) \|\| Dept. of Inf. Technol., Bannari Amman Inst. of Technol., Sathyamangalam (Wahi, A.)
Abstract	Natural scene categorization is a fundamental process of human vision that allows us to efficiently and rapidly analyze our surroundings. Thousands of images are generated every day, which implies the necessity to classify, organize and access them using an easy, faster and efficient way. Scene classification, the classification of images into semantic categories (e.g., coast, mountains, highways and streets) is a challenging and important problem nowadays. Many different approaches concerning scene classification have been proposed in the last few years. This paper presents a different approach to classify dasiaMIT-Streetpsila and dasiaMIT-highwaypsila scene categories using zernike moments and artificial neural networks. Back propagation is used for the paradigm of artificial neural networks. This work is implemented using various blocking models in the images. The comparative results are proving the importance of blocking and the efficiency of classifier towards scene classification problems. This complete work is carried out using real world data set.
Starting Page	1
Ending Page	5
File Size	3839930
Page Count	5
File Format	PDF
ISBN	9781424435944
DOI	10.1109/ICCCNET.2008.4787678
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Image segmentation Shape Layout Neurons Humans Fires Artificial neural networks Scene Categorization Feature extraction Robustness Zernike Moments
Content Type	Text
Resource Type	Article

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