A review on automatic image annotation techniques
Pattern Recognition
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Automatic image annotation by mining the web
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Image annotations based on semi-supervised clustering with semantic soft constraints
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Structural image retrieval using automatic image annotation and region based inverted file
Journal of Visual Communication and Image Representation
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Automatic image annotation has been intensively studied for content-based image retrieval recently. In this paper, we propose a novel approach for this task. Our approach first performs the segmentation of images into regions, followed by the clustering of regions, before learning the associations between concepts and region clusters using the set of training images with pre-assigned concepts. The main focus of this paper and our main contributions are as follows. First, in the learning stage, we perform clustering of regions into region clusters by incorporating pair-wise constraints derived by considering the language model underlying the annotations assigned to training images. Second, in the annotation stage, to alleviate the restriction of the independence assumption between region clusters, we develop a greedy selection and joining algorithm to find the independent sub-sets of region clusters and employ a semi-naïve Bayesian (SNB) model to compute the posterior probability of concepts given those independent sub-sets. Experimental results show that our proposed system utilizing these two strategies outperforms the state-of-the-art techniques in large image collection.