DocumentCode
1839409
Title
Landscape Image Composition Analysis Based on Image Processing and Curve Fitting
Author
Hengxiang Yang ; Xiujin Wang ; Lifang Bai
Author_Institution
Tianjin Key Lab. of Cognitive Comput. & Applic., Tianjin Univ., Tianjin, China
fYear
2013
fDate
21-23 June 2013
Firstpage
5
Lastpage
8
Abstract
The composition of the landscape image can convey the emotion of cameramen. Proposed in this article is one novel technique for landscape image composition analysis, which could lay the foundation for the further analysis of image emotion. This method is mainly aimed at analyzing the composition of landscape image with no foregrounds compos. Firstly, the image segmentation method based on edge detection and k-means clustering algorithm is used to extract the contour of each mountain. Secondly, curve fitting method is used to determine the curve type of each mountain in the landscape image. Finally, the normal distribution model is constructed to assign a weight to each pixel of the image from its location information, We can determine the composition of the whole image based on total weight comparison of each curve type which combined the contour information and location information. In this paper, we differentiated the curve from five types: Level-style, Inclined-style, Vertical-style, C-style and W-style. Corresponding, the composition of the whole image is divided into these five types. As a result, we can determine the composition type of landscape image. Experimental results demonstrate that this method is effective and meaningful in practice.
Keywords
curve fitting; edge detection; emotion recognition; image segmentation; normal distribution; pattern clustering; C-style; W-style; contour extraction; contour information; curve fitting; edge detection; image emotion; image processing; image segmentation method; inclined-style; k-means clustering algorithm; landscape image composition analysis; level-style; location information; normal distribution model; vertical-style; Algorithm design and analysis; Clustering algorithms; Curve fitting; Educational institutions; Image edge detection; Image segmentation; Object segmentation; curve fitting; image composition; image segmentation; normal distribution; weight assignment;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational and Information Sciences (ICCIS), 2013 Fifth International Conference on
Conference_Location
Shiyang
Type
conf
DOI
10.1109/ICCIS.2013.10
Filename
6642925
Link To Document