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» Image Classification With Kernelized Spatial-Context
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TMM
2010
135views Management» more  TMM 2010»
11 years 4 months ago
Image Classification With Kernelized Spatial-Context
Abstract--The goal of image classification is to classify a collection of unlabeled images into a set of semantic classes. Many methods have been proposed to approach this goal by ...
Guo-Jun Qi, Xian-Sheng Hua, Yong Rui, Jinhui Tang,...
TMM
2002
104views more  TMM 2002»
11 years 9 months ago
Spatial contextual classification and prediction models for mining geospatial data
Modeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov random fields (MRF) is a popular model for i...
Shashi Shekhar, Paul R. Schrater, Ranga Raju Vatsa...
ICIAP
2009
ACM
11 years 7 months ago
Tree Covering within a Graph Kernel Framework for Shape Classification
Abstract. Shape classification using graphs and skeletons usually involves edition processes in order to reduce the influence of structural noise. However, edition distances can no...
François-Xavier Dupé, Luc Brun
MICCAI
2009
Springer
12 years 11 months ago
Discriminative, Semantic Segmentation of Brain Tissue in MR Images
A new algorithm is presented for the automatic segmentation and classification of brain tissue from 3D MR scans. It uses discriminative Random Decision Forest classification and ta...
Zhao Yi, Antonio Criminisi, Jamie Shotton, Andr...
ACCV
2006
Springer
12 years 1 months ago
Multiple Similarities Based Kernel Subspace Learning for Image Classification
Abstract. In this paper, we propose a new method for image classification, in which matrix based kernel features are designed to capture the multiple similarities between images in...
Wang Yan, Qingshan Liu, Hanqing Lu, Songde Ma
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