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
会议名称Proc. 4th Asian Conference on Pattern Recognition (ACPR)
会议日期November 26-29, 2017
会议地点Nanjing, China
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).

关键词Genre Classification Of Web Images Low-level Feature Bag-of-words Hierarchical Classification
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/19973
专题模式识别国家重点实验室_模式分析与学习
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],2017.
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