Semi Supervised Feature Extraction for Filling Semantic Gap in Image Retrieval

Author

Jalali, Mahdi ; Sedghi, Tohid

Author_Institution

Naghadeh Branch, Islamic Azad Univ., Naghadeh, Iran

fYear

2011

fDate

16-17 Nov. 2011

Firstpage

1

Lastpage

4

Abstract

In this paper, a novel framework for combining the texture, shape information, beside that newly introduced transform for textural features are presented. This method is based on Spectral Function that provides a statistical description in the frequency domain of signals, and then the Spectal function (SF) of each signal is calculated by spectral analyzer (SSA). Features are energy and standard deviation of SF of signals got at different regions of bifrequency plane. This scheme shows high performance in Image sets. The experimental results are compared with previous works and are found to be encouraging.

Keywords

feature extraction; image retrieval; image texture; statistical analysis; SF; SSA; bifrequency plane; image retrieval; semantic gap filling; semisupervised feature extraction; shape information; signal frequency domain; spectral analyzer; spectral function; statistical description; textural features; texture information; Feature extraction; Force; Image edge detection; Image retrieval; Shape; Tiles; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Vision and Image Processing (MVIP), 2011 7th Iranian

Conference_Location

Tehran

Print_ISBN

978-1-4577-1533-4

Type

conf

DOI

10.1109/IranianMVIP.2011.6121537

Filename

6121537