Logo image clustering based on advanced statistics

In recent years, there has been a growing interest in the research of image content description techniques. Among those, image clustering is one of the most frequently discussed topics. Similar to image recognition, image clustering is also a high-level representation technique. However it focuses on the coarse categorization rather than the accurate recognition. Based on wavelet transform (WT) and advanced statistics, the authors propose a novel approach that divides various shaped logo images into groups according to the external boundary of each logo image. Experimental results show that the presented method is accurate, fast and insensitive to defects.

[1]  Wei Yi,et al.  Automatic Aircraft Recognition Using Maximum Likelihood Ratio Test , 2001, Active Media Technology.

[2]  Mohamed S. Kamel,et al.  Wavelet approximation-based affine invariant shape representation functions , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  A. Enis Çetin,et al.  Computationally efficient wavelet affine invariant functions for shape recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Mahmoud I. Khalil,et al.  Affine invariants for object recognition using the wavelet transform , 2002, Pattern Recognit. Lett..

[5]  A. N. Rajagopalan Image clustering using higher-order statistics , 2002 .

[6]  Wei-Ying Ma,et al.  Locality preserving clustering for image database , 2004, MULTIMEDIA '04.

[7]  Sulan Zhang,et al.  A Global Geometric Approach for Image Clustering , 2006, 18th International Conference on Pattern Recognition (ICPR'06).