Analysis and Extraction of Season Features in Natural Scenes for Retrieval

Author

Kun, Huang ; Maosheng, Lai

Author_Institution

Sch. of Manage., Beijing Normal Univ.

Volume

2

fYear

2006

fDate

Aug. 30 2006-Sept. 1 2006

Firstpage

43

Lastpage

46

Abstract

Most of the current image database systems depend on visual content to index images, which only provide a partial solution to the image retrieval problem. Natural Scenes are used popularly in our daily lives, which could always cause our strong feelings and senses. This paper discusses how to index natural scenes with season features, one of affective features, to improve the accuracy of image indexing and makes it more convenient to seek them. According to colorful natural scenes´ characteristics, it develops sky exclusion plus 1/2 area analysis to extract color features. At the same time, it carries out investigations to collect users´ impressions. Then, based on two kinds of features above, it establishes the mapping between color and season features by multiple linear regression, which could be used to index images automatically. Finally, through experiments the mapping is testified valid and correct to forecast and index the season features

Keywords

content-based retrieval; database indexing; feature extraction; image colour analysis; image retrieval; natural scenes; regression analysis; visual databases; color feature extraction; image database systems; image indexing; image retrieval problem; multiple linear regression; natural scene indexing; season feature extraction; sky exclusion plus 1/2 area analysis; visual content; Content based retrieval; Feature extraction; Image color analysis; Image databases; Image retrieval; Indexes; Indexing; Information retrieval; Layout; Linear regression; Natural scenes Kansei-based Image Indexing Multiple Linear Regression Color Histogram;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on

Conference_Location

Beijing

Print_ISBN

0-7695-2616-0

Type

conf

DOI

10.1109/ICICIC.2006.235

Filename

1691924