Researches on Hierarchical Image Retrieval Model Based on Wavelet Descriptor and Indexed by Half-Axes-Angle using R-Tree

Author(s):  
Hui-min Zhang ◽  
Qing-hai Wang ◽  
You-xun Kan ◽  
Jun-hui Liu ◽  
Yao-wan Gong
2008 ◽  
Vol 178 (22) ◽  
pp. 4301-4313 ◽  
Author(s):  
Woo-Cheol Kim ◽  
Ji-Young Song ◽  
Seung-Woo Kim ◽  
Sanghyun Park

2011 ◽  
Vol 255-260 ◽  
pp. 2057-2061
Author(s):  
Yong Mao Wang

This paper introduces an image retrieval model based on dimensionality reduction. The proposed model is divided into two main techniques, the first one is concerned with the feature extraction from image database, and the second one is performing a dimensionality reduction. In the first technique, the color histogram and Color Texture Moment are used to extract the color and texture features, respectively. In the second technique, Local Fisher Discriminant Analysis (LFDA) which is a supervised linear dimensionality reduction algorithm is used to performing dimensionality. LFDA combines the ideas of Fisher Discriminant Analysis (FDA) and Locality Preserving Projection (LPP). LFDA can preserve both manifold of data and discriminant information. Experiments demonstrate that the proposed image retrieval scheme based on dimensionality reduction can achieve satisfactory results.


1995 ◽  
Author(s):  
Takashi Kondoh ◽  
Masahiro Yamaguchi ◽  
Nagaaki Ohyama

2018 ◽  
Vol 2 (4) ◽  
pp. 351-367 ◽  
Author(s):  
Caihong Ma ◽  
Fu Chen ◽  
Jin Yang ◽  
Jianbo Liu ◽  
Wei Xia ◽  
...  

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