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Báo cáo hóa học: " Research Article Unsupervised Modeling of Objects and Their Hierarchical Contextual Interactions"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Unsupervised Modeling of Objects and Their Hierarchical Contextual Interactions | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2009 Article ID 184618 16 pages doi 10.1155 2009 184618 Research Article Unsupervised Modeling of Objects and Their Hierarchical Contextual Interactions Devi Parikh and Tsuhan Chen Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA 15213 USA Correspondence should be addressed to Devi Parikh dparikh@andrew.cmu.edu Received 11 June 2008 Accepted 2 September 2008 Recommended by Simon Lucey A successful representation of objects in literature is as a collection of patches or parts with a certain appearance and position. The relative locations of the different parts of an object are constrained by the geometry of the object. Going beyond a single object consider a collection of images of a particular scene category containing multiple recurring objects. The parts belonging to different objects are not constrained by such a geometry. However the objects themselves arguably due to their semantic relationships demonstrate a pattern in their relative locations. Hence analyzing the interactions among the parts across the collection of images can allow for extraction of the foreground objects and analyzing the interactions among these objects can allow for a semantically meaningful grouping of these objects which characterizes the entire scene. These groupings are typically hierarchical. We introduce hierarchical semantics of objects hSO that captures this hierarchical grouping. We propose an approach for the unsupervised learning of the hSO from a collection of images of a particular scene. We also demonstrate the use of the hSO in providing context for enhanced object localization in the presence of significant occlusions and show its superior performance over a fully connected graphical model for the same task. Copyright 2009 D. Parikh and T. Chen. This is an open access article distributed under the Creative Commons Attribution .