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Features' value range approach to enhance the throughput of texture classification

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