Robust shape retrieval through a novel statistical descriptor

Authors:
Tuantuan Wang;Tong Lu;Wenyin Liu
Affiliations:
State Key Lab for Novel Software Technology, Nanjing University, Nanjing, China and Jiangyin Institute of Information Technology of Nanjing University, China;State Key Lab for Novel Software Technology, Nanjing University, Nanjing, China and Jiangyin Institute of Information Technology of Nanjing University, China;State Key Lab for Novel Software Technology, Nanjing University, Nanjing, China and Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China
Venue:
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Year:
2010

Citing 11
Cited 0

Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
Independent component analysis: algorithms and applications

Neural Networks
Architectural symbol recognition using a network of constraints

Pattern Recognition Letters
A Constrained Approach to Multifont Chinese Character Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts

IEEE Transactions on Pattern Analysis and Machine Intelligence
A model for image generation and symbol recognition through the deformation of lineal shapes

Pattern Recognition Letters
Symbol Recognition via Statistical Integration of Pixel-Level Constraint Histograms: A New Descriptor

IEEE Transactions on Pattern Analysis and Machine Intelligence
Symbol Recognition with Kernel Density Matching

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Classification Using the Inner-Distance

IEEE Transactions on Pattern Analysis and Machine Intelligence
2D Shape Matching by Contour Flexibility

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Pixel-level Statistical Structural Descriptor for Shape Measure and Recognition

ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a novel statistical descriptor, Multiple References Histogram Matrix (MRHM), for robust shape retrieval, especially for degraded shape images. For each shape image, MRHM first generates uniform grids and filters noises in each grid by line Hough transformations and curve-fitting transformations. Then MRHM selects a reference for each grid and calculates its local distribution between the reference point and the shape pixels. Finally, all the local distributions are integrated into a global distribution matrix for matching symbols. Experimental results on the MPEG-7 Shape Silhouette Database and the GREC2005 Shape Database show that the proposed method's recognition rate for degraded shape images is greatly improved over a recent method (SFHM).