Feature transformation in compressed domain for content-based image retrieval

2003 ◽  
Author(s):  
Hau-San Wong ◽  
Horace H. Ip ◽  
Chun-Ip Chiu
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Compressed Domain ◽  
Feature Transformation

An Optimal Codebook for Content-Based Image Retrieval in JPEG Compressed Domain

2019 ◽  
Vol 44 (11) ◽  
pp. 9755-9767 ◽  
Author(s):  
Afshan Jamil ◽  
Muhammad Majid ◽  
Syed Muhammad Anwar
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Compressed Domain

scholarly journals Feature Extraction in JPEG domain along with SVM for Content Based Image Retrieval

2018 ◽  
Vol 7 (2.19) ◽  
pp. 1
Author(s):  
D. Mansoor Hussain ◽  
D. Surendran ◽  
A. Benazir Begum
Keyword(s):  
Image Retrieval ◽  
Research Work ◽  
Digital Camera ◽  
Feedback Mechanism ◽  
Multimedia Retrieval ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Compressed Domain ◽  
Security Systems ◽  
User Query

Content Based Image Retrieval (CBIR) applies computer vision methods for image retreival purposes from the databases. It is majorly based on the user query, which is in visual form rather than the traditional text form. CBIR is applied in different fields extending from surveillance to remote sensing, E-purchase, medical image processing, security systems to historical research and many others. JPEG, a very commonly used method of lossy compression is used to reduce the size of the image before being stored or transmitted. Almost every digital camera in the market are storing the captured images in jpeg format. The storage industry has seen many major transformations in the past decades while the retrieval technologies are still developing. Though there are some breakthroughs happened in text retrieval, the same is not true for the image and other multimedia retrieval. Specifically image retreival has witnessed many algorithms in the spatial or the raw domain but since majority of the images are stored in the JPEG format, it takes time to decode the compressed image before extracting features and retrieving. Hence, in this research work, we focus on extracting the features from the compressed domain itself and then utilize support vector machines (SVM) for improving the retrieval results. Our proof of concept shows us that the features extracted in compressed domain helps retrieve the images 43% faster than the same set of images in the spatial domain and the accuracy is improved to 93.4% through SVM based feedback mechanism.

Fusion of Deep Learning and Compressed Domain Features for Content-Based Image Retrieval

2017 ◽  
Vol 26 (12) ◽  
pp. 5706-5717 ◽  
Author(s):  
Peizhong Liu ◽  
Jing-Ming Guo ◽  
Chi-Yi Wu ◽  
Danlin Cai
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Compressed Domain

Content Based Image Retrieval using Feature Extraction in JPEG Domain and Genetic Algorithm

2017 ◽  
Vol 7 (1) ◽  
pp. 226
Author(s):  
Parvin N ◽  
Kavitha P
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithm ◽  
Image Retrieval ◽  
Weather Forecasting ◽  
Historical Research ◽  
Content Based Image Retrieval ◽  
The Internet ◽  
Compressed Domain ◽  
Security Systems ◽  
User Query

<p>Content-Based Image Retrieval (CBIR) aims at retrieving the images from the database based on the user query which is visual form rather than the traditional text form. The applications of CBIR extends from surveillance to remote sensing, medical imaging to weather forecasting, security systems to historical research and so on. Though extensive research is made on content based image retrieval in the spatial domain, we have most images in the internet which is JPEG compressed which pushes the need for image retrieval in the compressed domain itself rather than decoding it to raw format before comparison and retrieval. This research addresses the need to retrieve the images from the database based on the features extracted from the compressed domain along with the application of genetic algorithm in improving the retrieval results. The research focuses on various features and their levels of impact on improving the precision and recall parameters of the CBIR system. Our experimentation results also indicate that the CBIR features in compressed domain along with the genetic algorithm usage improves the results considerably when compared with the literature techniques.</p><p> </p>

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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