Abstract
How to search appropriate data from huge image pool has become vital issue. Because of easy availability of imaging devices, millions of images are being added to image pool every day. Image retrieval deals with searching relevant images from large image database. The paper presents novel image retrieval techniques based on discrete cosine transform applied on row mean, column mean and combination for feature extraction. Further the concept of image fragmentation is added to these to get total of 26 novel CBIR techniques. The proposed image retrieval techniques are applied to image database of 1000 images spread across 11 categories. Experimentation shows that taking row mean, column mean and combination improves the performance of image retrieval as compared to taking DCT of full image. Also fragmentation slightly helps in improving the image retrieval techniques.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
