Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Textural Features for Image Database Retrieval
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Robust Real-Time Face Detection
International Journal of Computer Vision
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Object Recognition by Integrating Multiple Image Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
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Automatic image annotation refers to the process of automatically labeling an image with a predefined set of keywords. Image annotation is an important step of content-based image retrieval (CBIR), which is relevant for many real-world applications. In this paper, a new algorithm based on multiple grid segmentation, entropy-based information and a Bayesian classifier, is proposed for an efficient, yet very effective, image annotation process. The proposed approach follows a two step process. In the first step, the algorithm generates grids of different sizes and different overlaps, and each grid is classified with a Naive Bayes classifier. In a second step, we used information based on the predicted class probability, its entropy, and the entropy of the neighbors of each grid element at the same and different resolutions, as input to a second binary classifier that qualifies the initial classification to select the correct segments. This significantly reduces false positives and improves the overall performance. We performed several experiments with images from the MSRC-9 database collection, which has manual ground truth segmentation and annotation information. The results show that the proposed approach has a very good performance compared to the initial labeling, and it also improves other scheme based on multiple segmentations.