Using structural patches tiling to guide human head-shoulder segmentation
Proceedings of the 20th ACM international conference on Multimedia
A relational kernel-based framework for hierarchical image understanding
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
QuMinS: Fast and scalable querying, mining and summarizing multi-modal databases
Information Sciences: an International Journal
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In this paper, we propose a Hierarchical Image Model (HIM) which parses images to perform segmentation and object recognition. The HIM represents the image recursively by segmentation and recognition templates at multiple levels of the hierarchy. This has advantages for representation, inference, and learning. First, the HIM has a coarse-to-fine representation which is capable of capturing long-range dependency and exploiting different levels of contextual information (similar to how natural language models represent sentence structure in terms of hierarchical representations such as verb and noun phrases). Second, the structure of the HIM allows us to design a rapid inference algorithm, based on dynamic programming, which yields the first polynomial time algorithm for image labeling. Third, we learn the HIM efficiently using machine learning methods from a labeled data set. We demonstrate that the HIM is comparable with the state-of-the-art methods by evaluation on the challenging public MSRC and PASCAL VOC 2007 image data sets.