Comparative study of content-base image retrieval and video fingerprinting

2010 ◽  
Author(s):  
Xiaofan Lin
Keyword(s):  
Comparative Study ◽  
Image Retrieval ◽  
Content Base Image Retrieval ◽  
Video Fingerprinting

scholarly journals A comparative study of different texture features for document image retrieval

2019 ◽  
Vol 121 ◽  
pp. 97-114 ◽  
Author(s):  
Fahimeh Alaei ◽  
Alireza Alaei ◽  
Umapada Pal ◽  
Michael Blumenstein
Keyword(s):  
Comparative Study ◽  
Image Retrieval ◽  
Texture Features ◽  
Document Image

Automatic Object Classification and Image Retrieval by Sobel Edge Detection

2014 ◽  
pp. 196-201
Keyword(s):  
Edge Detection ◽  
Comparative Study ◽  
Image Retrieval ◽  
Numerical Experiments ◽  
Object Classification ◽  
Similarity Coefficient ◽  
Cosine Similarity ◽  
Retrieval Algorithms ◽  
Sobel Edge Detection ◽  
Classification Tool

A comparative study of ability of the proposed novel image retrieval algorithms is performed to provide automated object classification invariant of rotation, translation, and scaling. Simple cosine similarity coefficient methods are analyzed. Considering applied cosine similarity coefficient methods, the two following approaches were tested and compared: the processing of the whole image and the processing of the image that contains edges extracted by the application of the Sobel edge detector. Numerical experiments on a real database sets indicate feasibility of the presented approach as an automated object classification tool without special image pre-processing.

scholarly journals Correlated Primary Visual Texton Histogram Features for Content Base Image Retrieval

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 46595-46616 ◽  
Author(s):  
Ahmad Raza ◽  
Hassan Dawood ◽  
Hussain Dawood ◽  
Sidra Shabbir ◽  
Rubab Mehboob ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Content Base Image Retrieval

scholarly journals Deep Face Image Retrieval: a Comparative Study with Dictionary Learning

2019 ◽  
Author(s):  
Ahmad S. Tarawneh ◽  
Ahmad B. Hassanat ◽  
Ceyhun Celik ◽  
Dmitry Chetverikov ◽  
M. Sohel Rahman ◽  
...  
Keyword(s):  
Comparative Study ◽  
Image Retrieval ◽  
Dictionary Learning ◽  
Face Image

Image Retrieval Using Mutual Information

2013 ◽  
Vol 427-429 ◽  
pp. 1537-1543 ◽  
Author(s):  
Ya Fen Wang ◽  
Feng Zhen Zhang ◽  
Shan Jian Liu ◽  
Meng Huang
Keyword(s):  
Mutual Information ◽  
Image Retrieval ◽  
Image Similarity ◽  
Theoretic Approach ◽  
Information Theoretic ◽  
Content Base Image Retrieval ◽  
Leibler Divergence ◽  
Similar Images ◽  
Information Theoretic Approach ◽  
The Given

In this paper, we study an information theoretic approach to image similarity measurement for content-base image retrieval. In this novel scheme, similarities are measured by the amount of information the images contained about one another mutual information (MI). The given approach is based on the premise that two similar images should have high mutual information, or equivalently, the querying image should convey high information about those similar to it. The method first generates a set of statistically representative visual patterns and uses the distributions of these patterns as images content descriptors. To measure the similarity of two images, we develop a method to compute the mutual information between their content descriptors. Two images with larger descriptor mutual information are regarded as more similar. We present experimental results, which demonstrate that mutual information is a more effective image similarity measure than those have been used in the literature such as Kullback-Leibler divergence and L2 norms.

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