scholarly journals A Comparative Analysis of Content based Image Retrieval

2021 ◽  
pp. 62-68
Author(s):  
Krishan Kumar ◽  
Sulekha Rani
Keyword(s):  
Comparative Analysis ◽  
Image Retrieval ◽  
Hybrid Approach ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Multimedia Technology ◽  
Large Set ◽  
User Request ◽  
The Right

With the development of multimedia technology, the rapid increasing usage of large image database becomes possible. To carry out its management and retrieval, Content-Based Image Retrieval (CBIR) is an effective method. It will be very difficult to manage this database of images stored at the remote servers. The right tool will be required which can process these images for different operations. These operations include searching etc. It will be difficult to classify the images into groups and then search each class for providing the image as the information against the user request query. The content based image retrieval is the most suitable way to identify the image from the large repository. It will search the image from the large set of images based on contents rather than the image name. It will be having less time to search the image from the large repository when the image is retrieved using content based. In the current research the hybrid approach for content based image retrieval is performed. This proposed procedure will be in the first step perform the classification of the image into multiple classes. The classes are prepared based on the attributes values.

scholarly journals A Review on LBP in Image Retrieval System for Future Enhancement and Vector Images

2020 ◽  
pp. 155-161
Author(s):  
K Rajalakshmi ◽  
V Krishna Dharshini ◽  
S Selva Meena
Keyword(s):  
Image Retrieval ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Large Set ◽  
Query Image ◽  
Matching Algorithm ◽  
Image Retrieval System ◽  
Similar Images ◽  
Vector Images ◽  
Similar Kind

Content-Based Image Retrieval is a process to retrieve the similar images from the large set of image database corresponding to the query image. In CBIR low level or pixel level features such as color, texture and shape of the images are extracted and on the basis of similarity matching algorithm the required similar kind of images are retrieved from the image database. To understand the evaluation and evolution of CBIR system various research was studied and various research is going on this way also. In this paper, we have discussed some of the popular pixel level feature extraction techniques for Content-Based Image Retrieval and we also present here about the performance of each technique.

Hybrid approach for content-based image retrieval

2021 ◽  
Vol 6 (1) ◽  
pp. 45
Author(s):  
S. Theetchenya ◽  
Somula Ramasubbareddy ◽  
S. Sankar ◽  
Syed Muzamil Basha
Keyword(s):  
Image Retrieval ◽  
Hybrid Approach ◽  
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.

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