A new algorithm for shape matching and pattern recognition using dynamic programming

TL;DR

This paper introduces a novel shape recognition method that transforms shape contours into strings and uses dynamic programming to find optimal alignments, improving shape matching accuracy.

Contribution

The paper presents a new shape matching algorithm that applies dynamic programming to string representations of shape contours for improved recognition.

Findings

Effective shape matching on MPEG-7 database

Accurate alignment of shape contours

Enhanced retrieval performance

Abstract

We propose a new method for shape recognition and retrieval based on dynamic programming. Our approach uses the dynamic programming algorithm to compute the optimal score and to find the optimal alignment between two strings. First, each contour of shape is represented by a set of points. After alignment and matching between two shapes, the contours are transformed into a string of symbols and numbers. Finally we find the best alignment of two complete strings and compute the optimal cost of similarity. In general, dynamic programming has two phases: the forward phase and the backward phase. In the forward phase, we compute the optimal cost for each subproblem. In the backward phase, we reconstruct the solution that gives the optimal cost. Our algorithm is tested in a database that contains various shapes such as MPEG-7.