Fast multi-class recognition of piecewise regular objects based on sequential three-way decisions and granular computing
The paper is focused on an application of sequential three-way decisions and granular computing to the problem of multi-class statistical recognition of the objects, which can be represented as a sequence of independent homogeneous (regular) segments. As the segmentation algorithms usually make it possible to choose the degree of homogeneity of the features in a segment, we propose to associate each object with a set of such piecewise regular representations (granules). The coarse-grained granules stand for a low number of weakly homogeneous segments. On the contrary, a sequence with a large count of high-homogeneous small segments is considered as a fine-grained granule. During recognition, the sequential analysis of each granularity level is performed. The next level with the finer granularity is processed, only if the decision at the current level is unreliable. The conventional Chow’s rule is used for a non-commitment option. The decision on each granularity level is proposed to be also sequential. The probabilistic rough set of the distance of objects from different classes at each level is created. If the distance between the query object and the next checked reference object is included in the negative region (i.e., it is less than a fixed threshold), the search procedure is terminated. Experimental results in face recognition with the Essex dataset and the state-of-the-art HOG features are presented. It is demonstrated, that the proposed approach can increase the recognition performance in 2.5–6.5 times, in comparison with the conventional PHOG (pyramid HOG) method.