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- W2371256167 abstract "Using single feature of images to describe the image content is one-sided because of insufficient information. To solve this problem,combing SIFT and KDES-G features to describe images is proposed by generating a joint vector as a newfeature vector. Considering images' spatial information,an image classification method based on the spatial visual dictionary is proposed. Images are first divided into sub-regions according to spatial pyramid,and the spatial visual dictionaries are respectively constructed by grouping features of each region into a number of clusters. The features of each region are coded by LLC based on its corresponding dictionary,and then the coded feature vectors are given different weights according to the different contribution of each region. After that,the feature vectors of different regions are concatenated and regarded as the final image description. Finally,a linear SVM is used to classify images. Experimental results showthat the proposed method has better performance and robustness compared with some state-of-the-art works." @default.
- W2371256167 created "2016-06-24" @default.
- W2371256167 creator A5068023868 @default.
- W2371256167 date "2015-01-01" @default.
- W2371256167 modified "2023-09-24" @default.
- W2371256167 title "An Image Classification Method Based on Multiple Level Spatial Visual Dictionary Ensemble" @default.
- W2371256167 hasPublicationYear "2015" @default.
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