Effective concept detection using Second order Co-occurence Flickr context similarity measure SOCFCS

Automatic photo annotation task aims to describe the semantic content by detecting high level concepts. Most existing approaches are performed by training independent concept detectors omitting the interdependencies between concepts. The obtained annotations are often not so satisfactory. Therefore, a process of annotation refinement is mondatory to improve the imprecise annotation results. Recently, harnessing the contextual correlation between concepts is shown to be an important resource to improve concept detection. In this paper, we propose a new context based concept detection process. For this purpose, we define a new semantic measure called Second order Co-occurence Flickr context similarity (SOCFCS), which aggregates the FCS values of common Flickr related-tags of two target concepts in order to calculate their relative semantic context relatedness (SCR). Our proposed measure is applied to build a concept network as the context space. A Random Walk with Restart process is performed over this network to refine the annotation results by exploring the contextual correlation among concepts. Experimental studies are conducted on ImageCLEF 2011 Collection containing 99 concepts. The results demonstrate the effectiveness of our proposed approach.