The goal is to develop approach specialized for mobile platform “ Compact Descriptor through Invariant Kernel Projection (CDIKP)” Feature descriptors are highly-compact ( 20-D ), in comparisons to the state-of-the-art (e.g. SIFT: 128-D , SURF: 64-D , and PCA-SIFT: 36-D ) Does not require any pre-training step (as required such as in PCA-SIFT) Robustly recognizes natural/artificial scene contents, such as visual tags, facility signs, product logos or labels, etc. Image Recognition for Mobile Phone ( Yun-Ta Tsai, Quan Wang, Suya You, “CDIKP: A Highly-Compact Local Feature Descriptor,” ICPR 2008 )

The goal is to develop approach specialized for mobile platform

“ Compact Descriptor through Invariant Kernel Projection (CDIKP)”

Feature descriptors are highly-compact ( 20-D ), in comparisons to the state-of-the-art (e.g. SIFT: 128-D , SURF: 64-D , and PCA-SIFT: 36-D )

Does not require any pre-training step (as required such as in PCA-SIFT)

Robustly recognizes natural/artificial scene contents, such as visual tags, facility signs, product logos or labels, etc.

CDIKP for Content Recognition System running on N95 phone