Multi-net System Configuration for Visual Object Segmentation by Error Backpropagation

摘要

This work proposes a new error backpropagation approach as a systematic way to configure and train the Multi-net System MNOD, a recently proposed algorithm able to segment a class of visual objects from real images. First, a single node of the MNOD is configured in order to best resolve the visual object segmentation problem using the best combination of parameters and features. The problem is then how to add new nodes in order to improve accuracy and avoid overfitting situations. In this scenario, the proposed approach employs backpropagation of error maps to add new nodes with the aim of increasing the overall segmentation performance. Experiments conducted on a standard dataset of real images show that our configuration method, using only simple edges and colors descriptors, leads to configurations that produced comparable results in visual objects segmentation.