Content-Aware Convolutional Neural Network for Object Recognition Task

  • Poernomo, Alvin (Department of Ubiquitous IT, Dongseo University) ;
  • Kang, Dae-Ki (Department of Computer & Information Engineering, Dongseo University)
  • Received : 2016.07.04
  • Accepted : 2016.07.30
  • Published : 2016.09.30


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.


Supported by : National Research Foundation of Korea (NRF)


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