Invariant multiscale triangle feature for shape recognition

Shape is an important visual characteristic in representing an object, and it is also an important part of human visual information. Shape recognition is an important research direction in pattern recognition and image understanding. However, it is a difficult problem to extract discriminative and robust shape descriptors in shape recognition. This is because there are very large deformations in the shape, such as geometric changes, intra-class variations, and nonlinear deformations. These factors directly influence the accuracy of shape recognition. To deal with the influence of these factors on the performance of shape recognition and enhance the accuracy of recognition, we present a novel shape description method called invariant multiscale triangle feature (IMTF) for robust shape recognition. This method uses two types of invariant triangle features to obtain the shape features of an object, and it can effectively combine the boundary and the interior characteristics of a shape, and hence can increase the distinguish ability of the shape. We conducted an extensive experimental analysis on some shape benchmarks. The results indicate that our method can achieve high recognition accuracy. The superiority of our method has been further demonstrated in comparison to the state-of-the-art shape descriptors. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

Shape is an important visual characteristic that represents objects, and shape recognition is a significant research direction. The invariant multiscale triangle feature (IMTF) is a novel method for robust shape recognition, which can effectively combine boundary and interior characteristics to improve recognition accuracy.