Condensed semantic tree model for image category representation

Author

Chen, Mianshu ; Fu, Ping ; Li, Yong ; Tan, Huiyuan

Author_Institution

Sch. of Commun. Eng., Jilin Univ., Changchun, China

Volume

4

fYear

2010

fDate

26-28 Feb. 2010

Firstpage

358

Lastpage

362

Abstract

This paper presents a condensed semantic tree model for representing image category. For a specific application area, a semantic concept space is defined. According to the annotation for an image, a real-value semantic vector is gained that describes the content of it. In order to represent image category, condensed semantic tree model is introduced. It is a triple level structure. The bottom level is a semantic concept mask, which selects those concepts relevant to semantic category. The middle level is composed of three semantic modules, which extract high-level semantic of an image. The top level analyzes the probability that an image is belong to a specific image category. Every semantic category has different model configuration. The experimental results illustrate that the effectiveness of the proposed condensed semantic tree model is good.

Keywords

image representation; trees (mathematics); condensed semantic tree model; high-level semantic; image annotation; image category representation; real-value semantic vector; semantic concept mask; semantic concept space; Content based retrieval; Image analysis; Image databases; Image retrieval; Information retrieval; Labeling; Ontologies; Probability; Statistical learning; Support vector machines; image category; semantic tree model; semantic vector;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4244-5585-0

Electronic_ISBN

978-1-4244-5586-7

Type

conf

DOI

10.1109/ICCAE.2010.5451664

Filename

5451664