Automatic Image Annotation with Cooperation of Concept-Specific and Universal Visual Vocabularies [chapter]

Yanjie Wang, Xiabi Liu, Yunde Jia
2010 Lecture Notes in Computer Science  
This paper proposes an automatic image annotation method based on concept-specific image representation and discriminative learning. Firstly, the concept-specific visual vocabularies are generated by assuming that localized features from the images with a specific concept are of the distribution of Gaussian Mixture Model (GMM). Each component in the GMM is taken as a visual token of the concept. The visual tokens of all the concepts are clustered to obtain a universal token set. Secondly, the
more » ... age is represented as a concept-specific feature vector by computing the average posterior probabilities of being each universal visual token for all the localized features and assigning it to corresponding conceptspecific visual tokens. Thus the feature vector for an image varies with different concepts. Finally, we implement image annotation and retrieval under a discriminative learning framework of Bayesian classifiers, Max-Min posterior Pseudo-probabilities (MMP). The proposed method were evaluated on the popular Corel-5K database. The experimental results with comparisons to state-of-the-art show that our method is promising.
doi:10.1007/978-3-642-11301-7_28 fatcat:pwzfni3scfebvauxsvneevy55u