ㆍ발행기관 : 한국인터넷방송통신학회ㆍ수록지정보 : International Journal of Advanced Smart Convergence / 5권 / 3호 / 1 ~ 7 페이지 ㆍ저자명 : Alvin Poernomo,Dae-Ki Kang
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.