Comparative study of content-base image retrieval and video fingerprinting

Author(s):  
Xiaofan Lin
2019 ◽  
Vol 121 ◽  
pp. 97-114 ◽  
Author(s):  
Fahimeh Alaei ◽  
Alireza Alaei ◽  
Umapada Pal ◽  
Michael Blumenstein

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.


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

Author(s):  
Ahmad S. Tarawneh ◽  
Ahmad B. Hassanat ◽  
Ceyhun Celik ◽  
Dmitry Chetverikov ◽  
M. Sohel Rahman ◽  
...  

2013 ◽  
Vol 427-429 ◽  
pp. 1537-1543 ◽  
Author(s):  
Ya Fen Wang ◽  
Feng Zhen Zhang ◽  
Shan Jian Liu ◽  
Meng Huang

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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