Content-based image retrieval on mobile devices

2005 ◽  
Author(s):  
Iftikhar Ahmad ◽  
Shafaq Abdullah ◽  
Serkan Kiranyaz ◽  
Moncef Gabbouj
Keyword(s):  
Image Retrieval ◽  
Mobile Devices ◽  
Content Based Image Retrieval

CONTENT-BASED IMAGE RETRIEVAL TRAINED BY ADABOOST FOR MOBILE APPLICATION

2006 ◽  
Vol 20 (04) ◽  
pp. 525-541 ◽  
Author(s):  
HWEI-JEN LIN ◽  
YANG-TA KAO ◽  
FU-WEN YANG ◽  
PATRICK S. P. WANG
Keyword(s):  
Image Retrieval ◽  
Mobile Devices ◽  
Mobile Application ◽  
Feature Space ◽  
Content Based Image Retrieval ◽  
Large Set ◽  
Training Process ◽  
Edge Information ◽  
Adaboost Algorithm ◽  
Wide Range

This paper proposes a Content-Based Image Retrieval (CBIR) system applicable in mobile devices. Due to the fact that different queries to a content-based image retrieval (CBIR) system emphasize different subsets of a large collection of features, most CBIR systems using only a few features are therefore only suitable for retrieving certain types of images. In this research we combine a wide range of features, including edge information, texture energy, and the HSV color distributions, forming a feature space of up to 1053 dimensions, in which the system can search for features most desired by the user. Through a training process using the AdaBoost algorithm9 our system can efficiently search for important features in a large set of features, as indicated by the user, and effectively retrieve the images according to these features. The characteristics of the system meet the requirements of mobile devices for performing image retrieval. The experimental results show that the performance of the proposed system is sufficiently applicable for mobile devices to retrieve images from a huge database.

FIXED CONTENT BASED IMAGE RETRIEVAL TECHNIQUE

2017 ◽  
Vol 5 (3) ◽  
pp. 54
Author(s):  
MOHAMMED ILIAS SHAIK ◽  
CHAUHAN DINESH ◽  
ESAPALLI SRINIVAS ◽  
PADIGE VINEETH ◽  
◽  
...  
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Retrieval Technique
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