Fast Genre Classification of Web Images Using Global and Local Features

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	Fast Genre Classification of Web Images Using Global and Local Features
	Liu GS(刘国帅); Feiyin; Zhen-Bo Luo; Cheng-Lin Liu; Liu CL(刘成林)
	2017
会议日期	November 26-29, 2017
会议地点	Nanjing, China
关键词	Genre Classification Of Web Images Low-level Feature Bag-of-words Hierarchical Classification
英文摘要	A number of images are present on the Web and the number is increasing every day. To effectively mine the contents embedded in Web images, it is useful to classify the images into different types so that they can be fed to different procedures for detailed analysis, such as text and non-text image discrimination. We herein propose a hierarchical algorithm for efficiently classifying Web images into four classes, namely, natural scene images, born-digital images, scanned and cameracaptured paper documents, which are the most prevalent image types on the Web. Our algorithm consists of two stages; the first stage extracts global features reflecting the distributions of color, edge and gradient, and uses a support vector machine (SVM) classifier for preliminary classification. Images assigned low confidence by the first-stage classifier is processed by the second stage, which further extracts local texture features represented in the Bag-of-Words framework and uses another SVM classifier for final classification. In addition, we design two fusion strategies to train the second classifier and generate the final prediction label depending on the usage of local features in the second stage. To validate the effectiveness of our proposed method, we also build a database containing more than 55,000 images from various sources. On our test image set, we obtained an overall classification accuracy of 98.4% and the processing speed is over 27FPS on an Intel(R) Xeon(R) CPU (2.90GHz).
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/19973]
专题	自动化研究所_模式识别国家重点实验室_模式分析与学习团队
通讯作者	Liu CL(刘成林)
作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Liu GS,Feiyin,Zhen-Bo Luo,et al. Fast Genre Classification of Web Images Using Global and Local Features[C]. 见:. Nanjing, China. November 26-29, 2017.