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Image Spam Detecting Based on Combinatorial and Statistical Classifier

Author: WangMuNi
Tutor: ZhangWeiFeng
School: Nanjing University of Posts and Telecommunications
Course: Computer Software and Theory
Keywords: Image spam Feature Extraction Local invariant feature Gaussian mixture model Cross-entropy
CLC: TP391.41
Type: Master's thesis
Year: 2012
Downloads: 18
Quote: 0
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Abstract


With the gradual arrival of the era of the Internet of Things , e - mail is still one of the most important communication tool . However, its byproducts - the emergence of the spam potential danger to people's lives . Which Image spam to promote anti-spam technology into a new field of study . How accurate and efficient detection Image spam is an urgent problem . The paper systematically analyzed the background of the image-based spam , Development and research significance based portfolio and statistical classification of image spam detection methods . The main work and contributions of the thesis is to : ( 1) local invariant feature SURF feature extraction algorithm to extract pictures and use Gaussian mixture model in statistical Gaussian mixture distribution fit to this feature of the image . As the distance between the Gaussian mixture distribution by means clustering algorithm to improve the K-means , cross entropy calculation standard Gaussian mixture model of the data set image clustering experiment to reduce the amount of calculation and improve the efficiency of the experiment . Final design and Gaussian mixture model classification based on cross- entropy , the new classifier has a better classification results verified by experiments . (2) image spam filtering system only for the image content characteristics or the image text feature , and easily lost image information , the classification accuracy is not high . Thesis model classification of the combination of using the stack has a combination of characteristics and content of the image characteristics of the image text , full use of the data carried by the image information , in particular, use different for different image feature classifier for classification , and then through the multi-level combination draw a comprehensive results . Through a variety of experiments , it is found that the use stack combination model image of the text and content features can obtain higher classification precision and recall rate .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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