Using grayscale images for object recognition with convolutional-recursive neural network

Publication Type:
Conference Proceeding
Citation:
2016 IEEE 6th International Conference on Communications and Electronics, IEEE ICCE 2016, 2016, pp. 321 - 325
Issue Date:
2016-09-07
Full metadata record
Files in This Item:
Filename Description Size
Using Grayscale Images.pdfPublished version410.61 kB
Adobe PDF
© 2016 IEEE. There is a common tendency in object recognition research to accumulate large volumes of image features to improve performance. However, whether using more information contributes to higher accuracy is still controversial given the increased computational cost. This work investigates the performance of grayscale images compared to RGB counterparts for visual object classification. A comparison between object recognition based on RGB images and RGB images converted to grayscale was conducted using a cascaded CNN-RNN neural network structure, and compared with other types of commonly used classifiers such as Random Forest, SVM and SP-HMP. Experimental results showed that classification with grayscale images resulted in higher accuracy classification than with RGB images across the different types of classifiers. Results also demonstrated that utilizing a small receptive field CNN and edgy feature selection on grayscale images can result in higher classification accuracy with the advantage of reduced computational cost.
Please use this identifier to cite or link to this item: