Earticle

초록

영어

Traditional image processing and pattern recognition research aimed at identifying the target image. As time goes by, on the basis of the recognition of the image, more and more research points to identify multiple targets in the image, and the corresponding block of the corresponding target is calibrated. Compared with the traditional image recognition, the problem of image annotation is a combination of multi classification and multi regression, which is more difficult and challenging. On the basis of deep convolutional neural network, this paper studies the image annotation algorithm based on region selection algorithm and support vector machine, and the algorithm is tested on the PASCAL VOC 2010 image data set. Experimental results show that compared with the current algorithm, this algorithm can be used to mark the image of multiple targets, the effect is obvious, and there is a great practical significance.

목차

AbstractAbstract
 1. Introduction
 2. Algorithm Framework of Image Annotation Based on Region Convolution Neural Network
 3. Algorithm Elaboration of Image Annotation Based on Regional Convolutional Neural Network
  3.1. The Acquisition of Image Annotation Candidate Frame
  3.2. Feature Extraction of Candidate Frame for Image Annotation
  3.3. Regression Result of Image Annotation
 4. Experiment and Result Analysis
 5. Conclusions
 Reference

키워드

저자

• Yuan Yuli [ College of Computer Science, Neijiang Normal University ]

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