Image Retrieval using DST and DST Wavelet Sectorization

Kekre, H. B.; Mishra, Durgesh Kumar

doi:10.14569/ijacsa.2011.020613

Cited by 3 publications

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Performance comparison of DFT and Hartley plane sectorization in CBIR

Kekre

Mishra

Gandhi

et al. 2013

2013 International Conference on Advances in Technology and Engineering (ICATE)

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This paper compares the retrieval performance of the CBIR system. In this system image features are extracted by means of Hartley and DFT transformed plane sectorization. Two different similarity measures named as sum of Absolute difference and the Euclidean distance are used for the matching the input image feature vectors with the feature vectors of the image database. The performances of the algorithm for both approaches, on different number of sectors are compared. The average precision-recall cross over point plot (PRCP) and the overall average PRCP performances have been compared for all sectors formed. This experiment makes use of the augmented Wang's image database. This database consists of 1056 images belonging to 12 different classes. It has been observed that the best overall average PRCP performance is achieved when the Hartley transformed image plane is sectored into 4 sectors and the AD is used as similarity measure.

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Performance comparison of DFT and Hartley plane sectorization in CBIR

Kekre

Mishra

Gandhi

et al. 2013

2013 International Conference on Advances in Technology and Engineering (ICATE)

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Performance Comparison of DFT and Hartley Plane Sectorization with its Wavelets across RGB, LUV and YCbCr Colour Planes in CBIR

Kekre¹,

Mishra²,

Gandhi³

et al. 2013

IJCA

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This paper uses the sectorization method on DFT and Hartley transforms along with sectorization of the DFT and Hartley wavelets over 3 different color spaces namely RGB, LUV and YCbCr. A comparison and evaluation of these methods is carried out and the results are drawn based on the retrieval rates of each class of images from the database. Three different similarity measures such as Precision, Recall, Precision-Recall Crossover are used for the matching the feature vectors of the query image with that of the images in the database. Euclidean distance and sum of Absolute difference are used for measuring the distances between the images. The average precision-recall cross-over point plot (PRCP) and the overall average PRCP performances have been compared for all the methods.

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