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Semantic image segmentation assigns a predefined class label to each pixel. This paper proposes a unified framework by using region bank to solve this task. Images are hierarchically segmented leading to region banks. Local features and high-level descriptors are extracted on each region of the bank. Discriminative classifiers are learned based on the histograms of feature descriptors computed from...
This paper presents a novel framework for semantic place labeling by formulating the problem in terms of energy minimization. A method based on graph cuts is used to minimize energy for a function of data cost and smoothness cost. While the data term aims at assigning visual observations to a set of pre-specified place categories, using appearance-based hierarchical classifiers, the smoothness term...
This paper reports our work on Chinese semantic role labeling, which takes advantage of hierarchical semantic knowledge from a common sense knowledge base named HowNet. On one hand, the words in lexical features such as predicate and head word are generalized with their hypernyms in HowNet. On the other hand, the hypernym-hyponym relation between sememes is used to capture the semantic similarity...
This paper presents a combination base machine learning approach to spatial semantic analysis in Chinese. The model consists of multiple pre-training classifiers and a gating mechanism for integrating the outputs of these classifiers. Then we use EM algorithm to train the parameters of the combining model. Finally the experimental results show an overall improvement on the standard corpus CPB.
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