Target recognition and shape retrieval method based on hierarchical description

Abstract

The invention discloses a target recognition and shape retrieval method based on hierarchical description. The method comprises the following steps of: extracting the profile feature of a target by a profile extracting algorithm, calculating a curvature value of each point on the profile target, extracting the angular point feature of the target by non-maximum value suppression, taking a profile segment corresponding to every two angular points as an overall feature describer of the target, carrying out hierarchical description on the profile points according to curvature, carrying out hierarchical description on the profile segments according to the importance degrees of value features, combining profile segments, the values of which are lower than evaluation thresholds, to form profile feature segments as partial feature describers of the target, carrying out normalization on the profile feature segments, and carrying out similarity measurement on the profile feature segments of different targets according to Shape Contexts distance. The method can be used for performing feature extraction on a target shape effectively, scale invariance, rotation invariance and translation invariance are achieved, the accuracy rate and the robustness in recognition are improved, and the computation complexity is reduced.

Description

technical field [0001] The invention relates to the technical field of object recognition and shape retrieval, in particular to an object recognition and shape retrieval method based on hierarchical description. Background technique [0002] How to make robot vision automatically recognize and recognize objects is a very challenging problem in the real world. The latest progress in this research is mainly to design intelligent shape descriptors to provide better similarity measures. Object recognition and shape retrieval are research hotspots in robot vision, and have been widely used in engineering, such as wide baseline matching, object category recognition, image and video retrieval, specific object recognition, robot navigation, scene classification, texture recognition and data mining and other fields. [0003] According to the source of the feature, the shape description method is divided into two categories: the method based on the contour and the method based on the...

AI Technical Summary

Problems solved by technology

Global features can describe the overall characteristics of the target. It is especially useful for simple target shapes and a single closed contour, but it is sensitive to local changes and prone to mismatches, such as Shape Contexts, Inner-Distance and Multi-scale Representation

Local features can overcome the above problems and are robust, because even if part of the contour is occluded or deformed, other local features can be matched and recognized, such as Shape Tree, Class Segment Sets, Contour Flexibility, but the computational complexity is high and will generate redundant information

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