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Articles by Bailing Zhang
Total Records ( 2 ) for Bailing Zhang
  Bailing Zhang
  Urinary Tract Infection (UTI) is a serious health problem affecting millions of people each year and it is significant to identify the causal agent prior to treatment. The bacteria typically associated with UTI include shape Eschericha coli, shape Klebsiella, shape Proteus mirabilis, shape Citrobacter freundii and shape Enterococcus sp. In recent years, a number of spectroscopic methods such as Fourier transform infrared (FT-IR) spectroscopy have been used to analyse the bacteria associated with UTI which are generally described as rapid whole organism fingerprinting. FT-IR typically takes only 10 sec per sample and generates holistic biochemical profiles from biological materials. In the past, multivariate analysis and artificial neural networks have been used to analyse and interpret the information rich data. In this study, The Support Vector Machine (SVM) applied to the FT-IR data for the automatic identification of UTI bacteria. Cross-validation test results indicate that the generalization performance of the SVM was over 98% to identify the UTI bacteria, compared to neural network's accuracy of 81%. Among the various multi-class SVM schemes tested, the Directed Acyclic Graph (DAG) method gives the best classification results. A Principal Component Analysis (PCA) based dimension-reduction could accelerate the training/testing time to a great extent, without deteriorating the identification performance.
  Bailing Zhang , Yonghua Song , Sheng-uei Guan and Yanchun Zhang
  Content-Based Image Retrieval (CBIR) of historic Chinese architecture images is an important area of research bridging society, culture and information technology. Most of the image features used in previous content-based image retrieval systems such as colour, texture and some simple shape descriptors are not effective in describing building images due to high variability in the heterogeneous architectural image collections. This study investigates content-based architectural image retrieval mainly by shape features. The recently proposed shape descriptor, Pyramid Histogram of Oriented Gradients (PHOG) features, counts occurrences of gradient orientation in localized portions of an image and has been proved as an efficient tool for providing spatial distribution of edges. Many existing image retrieval systems attempt to compare the query image with every target image in the database to find the top matching images, resulting in an essentially linear search which is prohibitive when the database is large. To solve the problem, it propose to introduce classification as the first stage in the retrieval system. Based on the PHOG features, it apply the Support Vector Machine (SVM) to automatically classify the ancient Chinese architecture images. Cross-validation test results indicate that the generalization performance of the SVM was over 60% compared to neural network's accuracy of 30% and kNN's accuracy 50%.
 
 
 
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