A New and Effective Image Retrieval Method Based on Combined Features
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
Content Based Image Retrieval Using Dominant Color Identification Based on Foreground Objects
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 03
Color Traits Transfer to Grayscale Images
ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
The Related Techniques of Content-Based Image Retrieval
ISCSCT '08 Proceedings of the 2008 International Symposium on Computer Science and Computational Technology - Volume 01
Image retrieval using augmented block truncation coding techniques
Proceedings of the International Conference on Advances in Computing, Communication and Control
Speaker identification using row mean vector of spectrogram
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
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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.