Blending texture features from multiple reference images for style transfer

2016 ◽  
Author(s):  
Hikaru Ikuta ◽  
Keisuke Ogaki ◽  
Yuri Odagiri
Keyword(s):  
Texture Features ◽  
Style Transfer ◽  
Reference Images ◽  
Multiple Reference

scholarly journals Proposed a Content-Based Image Retrieval System Based on the Shape and Texture Features

2019 ◽  
Vol 8 (11) ◽  
pp. 2185-2192
Keyword(s):  
Image Retrieval ◽  
Texture Feature ◽  
Texture Features ◽  
Visual Image ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Feature Extraction Method ◽  
Image Retrieval System ◽  
Reference Images ◽  
Similar Images

In this paper, we proposed a fusion feature extraction method for content based image retrieval. The feature is extracted by focusing on the texture and shape features of the visual image by using the Local Binary Pattern (LBP – texture feature) and Edge Histogram Descriptor (EHD – shape feature). The SVD is used for decreasing the number of the feature vector of images. The Kd-tree is used for reducing the retrieval time. The input to this system is a query image and Database (the reference images) and the output is the top n most similar images for the query image. The proposed system is evaluated by using (precision and recall) to measure the retrieval effectiveness. The values of the recall are between [43% –93%] and the average recall is 64.3%. The values of precision are between [30%-100%] and the average is 72.86% for the entire system and for both databases

scholarly journals A way to Calibrate a Colour Texture Feature

2020 ◽  
Vol 2020 (28) ◽  
pp. 150-155
Author(s):  
Hermine Chatoux ◽  
Noël Richard ◽  
Hela Jebali ◽  
François Lecellier ◽  
Christine Fernandez-Maloigne
Keyword(s):  
Texture Feature ◽  
Texture Features ◽  
Image Databases ◽  
Texture Descriptor ◽  
Spatial Frequencies ◽  
Reference Images

Several colour descriptors are presented each year. The existing protocols to evaluate and compare these descriptors are restricted to the use of image databases without information about the spatio-chromatic content. In this article, we present a first answer to calibrate a colour texture descriptor. By calibration, we intend evaluate the capacity of the descriptor to discriminate the non-uniform aspect according to different scales of samples. In order to assess all the possibilities in term of spatial frequencies and colour content, we propose to use reference images based on a fractal vector colour model. Three texture features are compared from this protocol allowing to express the interest of the proposed calibration sequence.

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