C
Chong Sze Tong
Researcher at Hong Kong Baptist University
Publications - 49
Citations - 1081
Chong Sze Tong is an academic researcher from Hong Kong Baptist University. The author has contributed to research in topics: Image processing & Non-negative matrix factorization. The author has an hindex of 16, co-authored 49 publications receiving 1042 citations. Previous affiliations of Chong Sze Tong include Hong Kong Examinations and Assessment Authority & Baptist College of Health Sciences.
Papers
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Journal ArticleDOI
Texture Classification Using Refined Histogram
TL;DR: A novel, efficient, and effective Refined Histogram (RH) for modeling the wavelet subband detail coefficients and a new image signature based on the RH model for supervised texture classification are presented.
Journal ArticleDOI
Fast fractal image encoding based on adaptive search
Chong Sze Tong,M. Pi +1 more
TL;DR: An optimal bit allocation scheme is formulated for the simultaneous quantizations of the usual scaling and the aforementioned unconventional affine parameter that has better properties than the conventional luminance offset.
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Statistical Wavelet Subband Characterization Based on Generalized Gamma Density and Its Application in Texture Retrieval
Siu Kai Choy,Chong Sze Tong +1 more
TL;DR: This paper proposes to adopt the three-parameter generalized gamma density (G¿D) for modeling wavelet detail subband histograms and for texture image retrieval and results reveal the superior performance of the proposed method compared with the current existing approaches.
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A Fast and Effective Model for Wavelet Subband Histograms and Its Application in Texture Image Retrieval
TL;DR: This paper presents a novel, effective, and efficient characterization of wavelet subbands by bit-plane extractions, which can be extracted directly from the code-block code-stream, rather than from the de-quantized wavelet coefficients, making this method particularly adaptable for image retrieval in the compression domain such as JPEG2000 format images.
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Adaptive approximate nearest neighbor search for fractal image compression
Chong Sze Tong,Man Wong +1 more
TL;DR: An improved formulation of approximate nearest neighbor search based on orthogonal projection and pre-quantization of the fractal transform parameters is presented, able to improve both the fidelity and compression ratio, while significantly reduce memory requirement and encoding time.