Abstract
We have introduced a novel idea of sectorization of DCT-DST plane of column wise transformed color images and feature vector generation with and without augmentation of extra row components. We have proposed augmentation of average value of zeroeth row component of DCT plane and absolute values of highest row components of DST plane column transformed color images to the feature vector. Two similarity measures such as sum of absolute difference and Euclidean distance are used and results are compared. The cross over point performance of overall average of precision and recall for both approaches on different sector sizes are compared. The DCT-DST plane sectorization is experimented on DCT-DST planes of transformed image The proposed algorithm is worked over database of 1055 images spread over 12 different classes. Overall Average precision and recall is calculated for the performance evaluation and comparison of 4, 8, 12 & 16 DCT-DST sectors done. We have also proposed two new performance measuring parameters namely LIRS (Length of initial relevant strings) and LSRR (Length of string to recover all relevant images). The use of sum of Absolute difference as similarity measure always gives lesser computational complexity and better relevant image retrieval rate compared to Euclidian distance.
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Kekre, H.B., Mishra, D. (2011). Sectorization of DCT-DST Plane for Column Wise Transformed Color Images in CBIR. In: Shah, K., Lakshmi Gorty, V.R., Phirke, A. (eds) Technology Systems and Management. Communications in Computer and Information Science, vol 145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20209-4_8
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DOI: https://doi.org/10.1007/978-3-642-20209-4_8
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