Features' value range approach to enhance the throughput of texture classification
Alireza Akoushideh, Babak Mazloom‐Nezhad Maybodi, Asadollah Shahbahrami · IET Image Processing · 2020
Abstract The definition of an image's category from a database with huge texture categories needs massive computation and time cost. Existing texture classification works focus on texture representation to improve the accuracy and efficiency of classification. This research wants to reduce the categories of the main classifier to decrease the comparison time of classification. To overcome computation time, a features' value range (FR) approach to enhance the throughput of texture classification is proposed. The proposed approach decreases the number of candidate categories as a pre‐classifier in a two‐step serial classification. With the decrease in the number of candidates, the main classifier can work on a few categories to find the final category. Here, configuration parameters are defined and some criteria are proposed for evaluating the FR approach. The performance of the FR is evaluated in the presence of different levels of Gaussian noise. Finally, it is shown that using effective features (EF) and hardware implementation approaches can extend the applicability of the FR approach. Experimental results depicted that the throughput of the final decision increased up to 14.85× with considerable reliability.