Earticle

초록

영어

In existing Convolutional Neural Network (CNNs) for object recognition task, there are only few efforts known to reduce the noises from the images. Both convolution and pooling layers perform the features extraction without considering the noises of the input image, treating all pixels equally important. In computer vision field, there has been a study to weight a pixel importance. Seam carving resizes an image by sacrificing the least important pixels, leaving only the most important ones. We propose a new way to combine seam carving approach with current existing CNN model for object recognition task. We attempt to remove the noises or the “unimportant” pixels in the image before doing convolution and pooling, in order to get better feature representatives. Our model shows promising result with CIFAR-10 dataset.

목차

Abstract
 1. Introduction
 2. Theory
  2.1 Convolutional Neural Network
  2.2 Seam carving
  2.3 Context-aware CNN with Seam Carving Layer
  2.4 Seam Carving Layer Backpropagation
 3. Experiment
 4.Result and Discussion
  4.1 Experiment result
  4.2 Discussion
 5. Conclusion
 6. Acknowledgement
 References

키워드

저자

• Alvin Poernomo [ Department of Ubiquitous IT, Dongseo University, 47 Jurye-ro, Sasang-gu, Busan 47011, Rep. of Korea ]
• Dae-Ki Kang [ Department of Computer & Information Engineering, Dongseo University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380