A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images

Semantic segmentation is the pixel-wise labelling of an image. Since the problem is defined at the pixel level, determining image class labels only is not acceptable, but localising them at the original image pixel resolution is necessary. Boosted by the extraordinary ability of convolutional neural networks (CNN) in creating semantic, high level and hierarchical image features; excessive numbers of deep learning-based 2D semantic segmentation approaches have been proposed within the last decade. In this survey, we mainly focus on the recent scientific developments in semantic segmentation, specifically on deep learning-based methods using 2D images. We started with an analysis of the public image sets and leaderboards for 2D semantic segmantation, with an overview of the techniques employed in performance evaluation. In examining the evolution of the field, we chronologically categorised the approaches into three main periods, namely pre-and early deep learning era, the fully convolutional era, and the post-FCN era. We technically analysed the solutions put forward in terms of solving the fundamental problems of the field, such as fine-grained localisation and scale invariance. Before drawing our conclusions, we present a table of methods from all mentioned eras, with a brief summary of each approach that explains their contribution to the field. We conclude the survey by discussing the current challenges of the field and to what extent they have been solved.Comment: Updated with new studie

Scale-Adaptive Video Understanding.

The recent rise of large-scale, diverse video data has urged a new era of high-level video understanding. It is increasingly critical for intelligent systems to extract semantics from videos. In this dissertation, we explore the use of supervoxel hierarchies as a type of video representation for high-level video understanding. The supervoxel hierarchies contain rich multiscale decompositions of video content, where various structures can be found at various levels. However, no single level of scale contains all the desired structures we need. It is essential to adaptively choose the scales for subsequent video analysis. Thus, we present a set of tools to manipulate scales in supervoxel hierarchies including both scale generation and scale selection methods. In our scale generation work, we evaluate a set of seven supervoxel methods in the context of what we consider to be a good supervoxel for video representation. We address a key limitation that has traditionally prevented supervoxel scale generation on long videos. We do so by proposing an approximation framework for streaming hierarchical scale generation that is able to generate multiscale decompositions for arbitrarily-long videos using constant memory. Subsequently, we present two scale selection methods that are able to adaptively choose the scales according to application needs. The first method flattens the entire supervoxel hierarchy into a single segmentation that overcomes the limitation induced by trivial selection of a single scale. We show that the selection can be driven by various post hoc feature criteria. The second scale selection method combines the supervoxel hierarchy with a conditional random field for the task of labeling actors and actions in videos. We formulate the scale selection problem and the video labeling problem in a joint framework. Experiments on a novel large-scale video dataset demonstrate the effectiveness of the explicit consideration of scale selection in video understanding. Aside from the computational methods, we present a visual psychophysical study to quantify how well the actor and action semantics in high-level video understanding are retained in supervoxel hierarchies. The ultimate findings suggest that some semantics are well-retained in the supervoxel hierarchies and can be used for further video analysis.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133202/1/cliangxu_1.pd