Detecting Image Spam Based on K-Labels Propagation Model
In order to detect image spam effectively, we propose a method based on a K-labels propagation model (KLPM) in this paper. Specifically, the speeded up robust features (SURF) of each image are extracted firstly. Then to standardize the features of each image, an improved means clustering algorithm i...
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| creator | Xiaoyan Qian Weifeng Zhang Yingzhou Zhang Guoqiang Zhou Ziyuan Wang |
| description | In order to detect image spam effectively, we propose a method based on a K-labels propagation model (KLPM) in this paper. Specifically, the speeded up robust features (SURF) of each image are extracted firstly. Then to standardize the features of each image, an improved means clustering algorithm is used to cluster these features and get the information of M cluster centers. Finally, after being labeled, all testing images are classified into spam images or ham images via the KLPM, which is based on a K-nearest neighbor (KNN) graph and a label propagating process. Experiments show that the precision of the proposed method can reach to 95%. Therefore, the method based on KLPM achieves a significant improvement in detecting Image Spam. |
| doi_str_mv | 10.1109/WISA.2013.40 |
| format | Conference Proceeding |
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Therefore, the method based on KLPM achieves a significant improvement in detecting Image Spam.</description><subject>Accuracy</subject><subject>Classification algorithms</subject><subject>Clustering algorithms</subject><subject>Feature extraction</subject><subject>Image Spam</subject><subject>Improved Means Clustering</subject><subject>Indexes</subject><subject>KLPM</subject><subject>KNN</subject><subject>label propagation</subject><subject>SURF</subject><subject>Testing</subject><subject>Unsolicited electronic mail</subject><isbn>9781479932184</isbn><isbn>1479932183</isbn><isbn>9781479932191</isbn><isbn>1479932191</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVjD1PwzAUAI0QEqhkY2PxH0jwsx1_jKVAiUhVpIIYq5f4OQpKmijJwr8HCRam091wjN2AyACEv_soDutMClCZFmcs8daBtt4rCR7O_7nTlyyZ508hBEjInZBXzD3QQvXSnhpe9NgQP4zY83ucKfDhxF_SEivqZv46DSM2uLQ_cTcE6q7ZRcRupuSPK_b-9Pi2eU7L_bbYrMu0BZsvKfjKhQDBVSLEHLASdS1Rh1pZlDboCkGQ08rUpKPJrTGgolQxWpt7I61asdvfb0tEx3Fqe5y-jsZaZxSobw1GRgg</recordid><startdate>201311</startdate><enddate>201311</enddate><creator>Xiaoyan Qian</creator><creator>Weifeng Zhang</creator><creator>Yingzhou Zhang</creator><creator>Guoqiang Zhou</creator><creator>Ziyuan Wang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201311</creationdate><title>Detecting Image Spam Based on K-Labels Propagation Model</title><author>Xiaoyan Qian ; Weifeng Zhang ; Yingzhou Zhang ; Guoqiang Zhou ; Ziyuan Wang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-19b8dd1d8b0df51ab0cc2a4dc37a27d4ba10e8436ce4f6576613f23ff77596273</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Accuracy</topic><topic>Classification algorithms</topic><topic>Clustering algorithms</topic><topic>Feature extraction</topic><topic>Image Spam</topic><topic>Improved Means Clustering</topic><topic>Indexes</topic><topic>KLPM</topic><topic>KNN</topic><topic>label propagation</topic><topic>SURF</topic><topic>Testing</topic><topic>Unsolicited electronic mail</topic><toplevel>online_resources</toplevel><creatorcontrib>Xiaoyan Qian</creatorcontrib><creatorcontrib>Weifeng Zhang</creatorcontrib><creatorcontrib>Yingzhou Zhang</creatorcontrib><creatorcontrib>Guoqiang Zhou</creatorcontrib><creatorcontrib>Ziyuan Wang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xiaoyan Qian</au><au>Weifeng Zhang</au><au>Yingzhou Zhang</au><au>Guoqiang Zhou</au><au>Ziyuan Wang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Detecting Image Spam Based on K-Labels Propagation Model</atitle><btitle>2013 10th Web Information System and Application Conference</btitle><stitle>wisa</stitle><date>2013-11</date><risdate>2013</risdate><spage>170</spage><epage>175</epage><pages>170-175</pages><isbn>9781479932184</isbn><isbn>1479932183</isbn><eisbn>9781479932191</eisbn><eisbn>1479932191</eisbn><coden>IEEPAD</coden><abstract>In order to detect image spam effectively, we propose a method based on a K-labels propagation model (KLPM) in this paper. Specifically, the speeded up robust features (SURF) of each image are extracted firstly. Then to standardize the features of each image, an improved means clustering algorithm is used to cluster these features and get the information of M cluster centers. Finally, after being labeled, all testing images are classified into spam images or ham images via the KLPM, which is based on a K-nearest neighbor (KNN) graph and a label propagating process. Experiments show that the precision of the proposed method can reach to 95%. Therefore, the method based on KLPM achieves a significant improvement in detecting Image Spam.</abstract><pub>IEEE</pub><doi>10.1109/WISA.2013.40</doi><tpages>6</tpages></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Accuracy Classification algorithms Clustering algorithms Feature extraction Image Spam Improved Means Clustering Indexes KLPM KNN label propagation SURF Testing Unsolicited electronic mail |
| title | Detecting Image Spam Based on K-Labels Propagation Model |
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