Earticle

초록

영어

Image classification is an important task in computer vision. The methods based on spatial information generally employ some low-level features for image classification, such as gray scale, color, texture and location. It is difficult for vision system to understand and the single feature is too limited to obtain correct classification results. In this paper, an algorithm based on multi-kernel feature learning is proposed and used for image classification. First, the kernel function is used to produce a kernel descriptor, which aggregates the pixel attributes into patch-level features; Then, through the multi-kernel learning, these descriptors are further aggregated to obtain hierarchical multi-feature descriptors; Finally, the label of each image is given by the fusion strategy of on multi-classifiers, which effectively utilizes the advantages of multi-kernel learning and takes the complementary among the classifiers into account. The experimental results show that the proposed method is efficient in promoting the classification results.

목차

Abstract
 1. Introduction
 2. Hierarchical Match Multi-Kernel
  2.1. Histograms of Oriented Gradient (HOG)
  2.2. Local Binary Pattern (LBP)
 3. Feature Extraction via Hierarchical Multi-Kernel
  3.1. Kernel Descriptor
  3.2. Hierarchical Multi-Kernel
 4. Multi-Classifier Fusion
 5. Experimental Results
  5.1. Datasets
  5.2. Classification Results
 6. Conclusions
 References

키워드

저자

• Fenxia Wu [ School of Computer Science, Xianyang Normal University, Xianyang, Shaanxi, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605