A new algorithm for shape matching and pattern recognition using dynamic programming
This work addresses shape matching for pattern recognition applications, but appears incremental as it applies a known dynamic programming approach to shape data.
The paper tackles shape recognition and retrieval by proposing a dynamic programming algorithm to compute optimal alignments between shapes represented as strings, and tests it on the MPEG-7 database.
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.