Semi-naive Bayesian classifier
EWSL-91 Proceedings of the European working session on learning on Machine learning
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Building semantic perceptron net for topic spotting
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Region based image annotation through multiple-instance learning
Proceedings of the 13th annual ACM international conference on Multimedia
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Building a Compact Relevant Sample Coverage for Relevance Feedback in Content-Based Image Retrieval
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Composition based semantic scene retrieval for ancient murals
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
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We propose a novel approach for auto image annotation. In our approach, we first perform the segmentation of images into regions, followed by clustering of regions, before learning the relationship between concepts and region clusters using the set of training images with pre-assigned concepts. The main focus of this paper is two-fold. First, in the learning stage, we perform clustering of regions into region clusters by incorporating pair-wise constraints which are derived by considering the language model underlying the annotations assigned to training images. Second, in the annotation stage, we employ a semi-naïve Bayes model to compute the posterior probability of concepts given the region clusters. Experiment results show that our proposed system utilizing these two strategies outperforms the state-of-the-art techniques in annotating large image collection.