Semantic context learning and representation with spatial Markov kernels for image annotation and categorization

Abstract

With the rapid growth of image archives, many content-based image retrieval and annotation systems have been developed for effectively indexing and searching these images. However, due to the semantic gap problem, these systems are still far from satisfactory for practical use. Hence, bridging the semantic gap has been an area of intensive research, in which several influential approaches that based upon an intermediate representation such as bag-of-words (BOW) have demonstrated major successes. In most previous work,, the semantic context between visual words in BOW is usually ignored or not exploited for the retrieval and annotation. To resolve this problem, we have developed a series of approaches to semantic context extraction and representation that is based on the Markov models and kernel methods. To our knowledge, this is the first application of kernel methods and 2D Markov models simultaneously to image categorization and annotation which have been shown through experiments on standard benchmark datasets that they are able to outperform several state-of-the-art methods.