Fast genre classification of web images using global and local features

CAAI Transactions on Intelligence Technology

article

Guo-Shuai Liu¹  Rui-Qi Wang¹  Fei Yin¹  Jean-Marc Ogier³  Cheng-Lin Liu¹ 

[1] National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences;School of Artificial Intelligence, University of Chinese Academy of Sciences;L3i Laboratory, Faculty of Science and Technology, University of La Rochelle;Center for Excellence of Brain Science and Intelligence Technology

关键词: support vector machines;    image texture;    feature extraction;    image classification;    image colour analysis;    cameras;    support vector machine classifier;    first-stage classifier;    SVM classifier;    second-stage classifier;    camera-captured paper documents;    global feature extraction;    local texture feature extraction;    natural scene imaging;    fast genre web image classification;    born-digital imaging;    nontext image discriminations;    text image discriminations;    image colour analysis;    image fusion;    bag-of-words framework;    Intel(R) Xeon(R) central processing unit;    frequency 2.9 GHz;    (B6135E) Image recognition;    (B7230G) Image sensors;    (C5260B) Computer vision and image processing techniques;    (C6170K) Knowledge engineering techniques;   

DOI  :  10.1049/trit.2018.1018

学科分类：数学（综合）

来源: Wiley

【 摘 要 】

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. The authors herein propose a hierarchical algorithm for efficiently classifying web images into four classes. Their 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 are 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, they design two fusion strategies to train the second-stage classifier and generate the final prediction depending on the usage of local features in the second stage. To validate the effectiveness of proposed method, they built a database containing more than 55,000 images from various sources. On their test image set, they obtained an overall classification accuracy of 98.4% and the processing speed is over 27 fps on an Intel(R) Xeon(R) central processing unit (2.90 GHz).

【 授权许可】

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