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Full-Text Articles in Physical Sciences and Mathematics
Greedy Approximation Of Kernel Pca By Minimizing The Mapping Error, Peng Cheng, Wanqing Li, Philip Ogunbona
Greedy Approximation Of Kernel Pca By Minimizing The Mapping Error, Peng Cheng, Wanqing Li, Philip Ogunbona
Professor Philip Ogunbona
In this paper we propose a new kernel PCA (KPCA) speed-up algorithm that aims to find a reduced KPCA to approximate the kernel mapping. The algorithm works by greedily choosing a subset of the training samples that minimizes the mean square error of the kernel mapping between the original KPCA and the reduced KPCA. Experimental results have shown that the proposed algorithm is more efficient in computation and effective with lower mapping errors than previous algorithms.
Detecting Humans Under Occlusion Using Variational Mean Field Method, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li
Detecting Humans Under Occlusion Using Variational Mean Field Method, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li
Professor Philip Ogunbona
This paper proposes a human detection method using variational mean field approximation for occlusion reasoning. In the method, parts of human objects are detected individually using template matching. Initial detection hypotheses with spatial layout information are represented in a graphical model and refined through a Bayesian estimation. In this paper, mean field method is employed for such an estimation. The proposed method was evaluated on the popular CAVIAR-INRIA dataset. Experimental results show that the proposed algorithm is able to detect humans in severe occlusion within reasonable processing time.
On The Step Response Of The Dct, Jim Andrew, Philip Ogunbona
On The Step Response Of The Dct, Jim Andrew, Philip Ogunbona
Professor Philip Ogunbona
We show that the discrete cosine transform (DCT) is the best orthogonal transform, in terms of energy packing efficiency, for coding input steps of uniformly distributed random phase. Over sufficiently small block sizes, edges in an image can be modeled as such step inputs. This characteristic of the DCT, coupled with its high energy packing efficiency for highly correlated data, helps explain the impressive performance of the DCT for image compression.
On The Combination Of Local Texture And Global Structure For Food Classification, Zhimin Zong, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li
On The Combination Of Local Texture And Global Structure For Food Classification, Zhimin Zong, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li
Professor Philip Ogunbona
This paper proposes a food image classification method using local textural patterns and their global structure to describe the food image. In this paper, a visual codebook of local textural patterns is created by employing Scale Invariant Feature Transformation (SIFT) interest point detector with the Local Binary Pattern (LBP) feature. In addition to describing the food image using local texture, the global structure of the food object is represented as the spatial distribution of the local textural structures and encoded using shape context. We evaluated the proposed method on the Pittsburgh Fast-Food Image (PFI) dataset. Experimental results showed that the …