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New Index
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Item
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Semantic Labeling of natural Scene Images Using Color Features
http://hdl.handle.net/20.500.12678/0000007728
http://hdl.handle.net/20.500.12678/0000007728009462b1-e46c-4b22-9ddc-d9537519c66c
0af831e9-f74d-425d-8de2-b8fb36a150d2
Name / File | License | Actions |
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![]() |
Publication type | ||||||
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Journal article | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Semantic Labeling of natural Scene Images Using Color Features | |||||
Language | en | |||||
Publication date | 2018-08-28 | |||||
Authors | ||||||
Kyawt Kyawt Htay | ||||||
G R Sinha | ||||||
Hanni Htun | ||||||
New Ni Kyaw | ||||||
Description | ||||||
Scene image classification systems firstly need to locate the objects, and then classify the whole image. The color feature is important to describe the properties of an image surface. The paper presents a framework for scene images to label local regions using color features. The paper uses maker-controlled watershed algorithm to segment the input image into regions. This paper uses the segmented regions as a basic input unit, and then extracts Color Histogram (CH) and Color Moment (CM) features in HSV space. This system performs labeling using 3-layer Feed Forward Neural Network (FFNN) classifier. The system tests accuracy on public Microsoft Research Cambridge (MSRC) 9-class dataset. | ||||||
Keywords | ||||||
Scene classification, Color features, Color moment, Color histogram, Semantic concepts | ||||||
Journal articles | ||||||
Issue 2 | ||||||
CSVTU International Journal of Biotechnology, Bioinformatics and Biomedical | ||||||
Pages 67–71 | ||||||
Volume 4 |