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In recent years, high-level image representation is gaining popularity in image classification and retrieval tasks. This paper proposes an efficient scheme known as semantic context model to derive high-level image descriptors well suited for the retrieval operation. Semantic context model uses an undirected graphical model based formulation which jointly exploits low-level visual features and contextual information for classifying local image blocks into some predefined concept classes. Contextual information involves concept co-occurrences and their spatial correlation statistics. More expressive potential functions are introduced to capture the structural dependencies among various semantic concepts. The proposed framework proceeds in three steps. Initially, optimal values of model parameters that impose spatial consistency of concept labels among local image blocks are learned from the training data. Then, the semantics associated with the constituent blocks of an unseen image are inferred using an improved message-passing algorithm. Finally, a compact but discriminative image signature is derived by integrating the frequency of occurrence of various regional semantics. Experimental results on various benchmark datasets show that semantic context model can effectively resolve local ambiguities and consequently improve concept recognition performance in complex images. Moreover, the retrieval efficiency of the new semantics based image feature is found to be much better than state-of-the-art approaches.
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