Simultaneous Image Classification And Annotation
CVPR: 2009 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-4(2009)
摘要
Image classification and annotation are important problems in computer vision, but rarely considered together Intuitively, annotations provide evidence for the class label, and the class label provides evidence for annotations. For example, an image of class highway is more likely annotated with words "road," "car" and "traffic" than words "fish," "boat," and "scuba." In this paper we develop a new probabilistic model for jointly modeling the image, its class label, and its annotations. Our model treats the class label as a global description of the image, and treats annotation terms as local descriptions of parts of the image. Its underlying probabilistic assumptions naturally integrate these two sources of information. We derive an approximate inference and estimation algorithms based on variational methods, as well as efficient approximations for classifying and annotating new images. We examine the performance of our model on two real-world image data sets, illustrating that a single model provides competitive annotation performance, and superior classification performance.
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关键词
computer science,predictive models,variational method,probability,probabilistic model,image classification,layout,image annotation,indexing,computer vision
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