Assessing the use of similarity distance measurement in shape recognition

Distance measure is one of the techniques widely used to measure the similarity between two feature matrices of objects. The objective of this paper is to explore researches on applied distance measures in shape-based recognition. In distance measures computation, patterns that are similar will have a small distance while uncorrelated pattern in the feature space will have a far a part distance. The search for effective distance measures of shape recognition is always active as each measure suffers certain drawbacks and it must be selected appropriately to handle chosen shape features of the objects. Thus in this paper, the Chord, Cosine, Euclidean, Mahalanobis, Trigonometric and Jaccard distance were reviewed and discussed in terms of their contributions, measures strengths and weaknesses. It was found that Jaccard and Mahalanobis have their strengths that they were selected to guide in justifying and identifying appropriate distance measures of our future work on two dimensional sketching images. The new distance measure is expected to perform better and capable to obtain significance degree of accuracy and recognition rate for real time recognition for automatic classifier.

[1]  Yang Mingqiang,et al.  Shape Matching and Object Recognition Using Chord Contexts , 2008, 2008 International Conference Visualisation.

[2]  Manuele Bicego,et al.  A Hidden Markov Model approach for appearance-based 3D object recognition , 2005, Pattern Recognit. Lett..

[3]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[4]  Shigeki Sagayama,et al.  Pen pressure features for writer-independent on-line handwriting recognition based on substroke HMM , 2002, Object recognition supported by user interaction for service robots.

[5]  Beiji Zou,et al.  Shape-Based Trademark Retrieval Using Cosine Distance Method , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.

[6]  H. Nemmour,et al.  New Jaccard-Distance Based Support Vector Machine Kernel for Handwritten Digit Recognition , 2008, 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications.

[7]  S.M. Hazarika,et al.  Enhanced Shape Context for Object Recognition , 2007, 15th International Conference on Advanced Computing and Communications (ADCOM 2007).

[8]  Mohammad Reza Daliri,et al.  Robust symbolic representation for shape recognition and retrieval , 2008, Pattern Recognit..

[9]  Sven J. Dickinson,et al.  Object Representation and Recognition , 1999 .

[10]  Jun Guo,et al.  Efficient Computation of Mahalanobis Distance in Financial Hand-Written Chinese Character Recognition , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[11]  Jianmin Jiang,et al.  Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking , 2011, Pattern Recognit. Lett..

[12]  Niamh Nic Daeid,et al.  Examining the effects of paper type, pen type, writing pressure and angle of intersection on white and dark dominance in ESDA impressions of sequenced strokes--an application of the likelihood ratio. , 2008, Forensic science international.

[13]  Hua Li,et al.  Similarity Measurement Based on Trigonometric Function Distance , 2006, 2006 First International Symposium on Pervasive Computing and Applications.

[14]  Tai-hoon Kim,et al.  Use of Artificial Neural Network in Pattern Recognition , 2010 .

[15]  X. Zhang,et al.  Object representation and recognition in shape spaces , 2003, Pattern Recognit..

[16]  Kpalma Kidiyo,et al.  A Survey of Shape Feature Extraction Techniques , 2008 .

[17]  Yan Zhang,et al.  On the Euclidean distance of images , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Guojun Lu,et al.  Shape-based image retrieval using generic Fourier descriptor , 2002, Signal Process. Image Commun..

[19]  Chaur-Chin Chen,et al.  Similarity Measurement Between Images , 2005, COMPSAC.

[20]  Xiaofeng Wang,et al.  Shape recognition based on neural networks trained by differential evolution algorithm , 2007, Neurocomputing.

[21]  Pepe Siy,et al.  Robust shape similarity retrieval based on contour segmentation polygonal multiresolution and elastic matching , 2005, Pattern Recognit..