CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
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
Conference NameProc. 4th Asian Conference on Pattern Recognition (ACPR)
Conference DateNovember 26-29, 2017
Conference PlaceNanjing, China
AbstractA 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).

KeywordGenre Classification Of Web Images Low-level Feature Bag-of-words Hierarchical Classification
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19973
Collection模式识别国家重点实验室_模式分析与学习
Corresponding AuthorLiu CL(刘成林)
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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