Pixel representations, sampling, and label correction for semantic part detection

Abstract

Semantic part detection within an object is of importance in the field of computer vision. This study proposes a novel approach to semantic part detection that starts by employing a convolutional neural network to concatenate a selection of feature maps from the network into a long vector for pixel representation. Using this dedicated pixel representation, we implement a range of techniques, such as Poisson disk sampling for pixel sampling and Poisson matting for pixel label correction. These techniques efficiently facilitate the training of a practical pixel classifier for part detection. Our experimental exploration investigated various factors that affect the model’s performance, including training data labeling (with or without the aid of Poisson matting), hypercolumn representation dimensionality, neural network architecture, post-processing techniques, and pixel classifier selection. In addition, we conducted a comparative analysis of our approach with established object detection methods.