Abstract：Firstly, a fully unsupervised segmentation algorithm with improved k-means is employed to divide images into regions. Then, a method of information bottleneck is proposed to cluster the segmented region and the relationship between image semantic concept and clustering regions is established. Image segmentation is used in the unannotated image so that the conditional probability of each semantic concept can be calculated under the condition of segmenting region. The image semantics is automatically annotated by keywords with maximal conditional probability. The system is implemented and tested on a 500-image database, and the experimental results show that the effectiveness of the proposed method outperforms others.
夏利民，谭立球，钟洪. 基于信息瓶颈算法的图像语义标注*[J]. 模式识别与人工智能, 2008, 21(6): 812-818.
XIA Li-Min, Tan Li-Qiu, ZHONG Hong. Semantic Annotation of Image Based on Information Bottleneck Method. , 2008, 21(6): 812-818.
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