On the Influence of the Color Model for Image Boundary Detection Algorithms based on Convolutional Neural Networks

Image analysis and understanding are challenging tasks, usually having segmentation as a major step. Boundary detection is a type of segmentation which aims to highlight the boundaries of the objects in a scene. Models based on Convolutional Neural Networks (CNN) have presented promising results for boundary detection, where the input usually is the entire image or some patches, often described in the RGB color model. In this paper, we provide a qualitative analysis of boundary detection algorithms based on CNN but considering images in different color models. We have used the color models RGB, Lab, Luv, dRdGdB, YO1O2 and HSV for this analysis. The Holistically-Nested Edge Detection (HED) and Convolutional Encoder Decoder Network (CEDN) are the CNN's chosen due to their high performance. The benchmark BSDS is the boundary detection evaluator. Experiments show that the results of the edge detection process tend to be similar when training the CNN with weights randomly initialized, regardless of the color model used. For the HED architecture, the use of Lab and Luv color models has resulted in a significant improvement to the case of transfer learning and fine-tuning of weights.