Condensed semantic tree model for image category representation
Mianshu Chen, Ping Fu, Yong Li, Huiyuan Tan · 2010
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.