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CUD a-accelerated fast training of Locally connected Neural Pyramid using YIQ color coding
A. Kurhade, A. Thakare,
Published in IEEE Computer Society
Pages: 1116 - 1121
To achieve a panoptic machine vision, recognition of images from disparate classes like person, car, building, et cetera is of primal importance. The Locally-connected Neural Pyramid (LCNP) was proposed earlier to achieve a robust and a time efficient training of large datasets of images from these disparate classes. The objective of this paper is to propose a technique for fast training of th e LCNP. YIQ co ding is used to ex tract the color based information of the images as it separates the color information from the luminance information. As R GB to YIQ conversion is an embarrassingly parallel situation, this recoding can give a tremendous speed-up over the previous approach- where PCA de-correlation of RGB channels was carried out. Also, the use of YIQ coding has entailed a reduction in the complexity of the LCNP, thu s, reducing the computations considerably. This will further boost the time performance of the training. Despite a considerable reduction in the complexity of the LCNP and the use of YIQ coding, the recognition performance achieved by this approach is similar to the previous approach. A recognition rate of 85.62% is achieved for the testing samples of the LabelMe-12-50K dataset. We p ropose that if the previous method of de-correlating RGB ch annels using PCA is rep laced with YIQ coding, tremendous speed-up will be achieved. © 2014 IEEE.
About the journal
JournalData powered by TypesetSouvenir of the 2014 IEEE International Advance Computing Conference, IACC 2014
PublisherData powered by TypesetIEEE Computer Society