It is well accepted that image segmentation can benefit from utilizing multilevel cues. The paper focuses on utilizing the FCNN-based dense semantic predictions in the bottom-up image segmentation, arguing to take semantic cues into account from the very beginning. By this we can avoid merging regions of similar appearance but distinct semantic categories as possible. The semantic inefficiency problem is handled. We also propose a straightforward way to use the contour cues to suppress the noise in multilevel cues, thus to improve the segmentation robustness. The evaluation on the BSDS500 shows that we obtain the competitive region and boundary performance. Furthermore, since all individual regions can be assigned with appropriate semantic labels during the computation, we are capable of extracting the adjusted semantic segmentations. The experiment on Pascal VOC 2012 shows our improvement to the original semantic segmentations which derives directly from the dense predictions.
In the bottom-up merging segmentation approaches @cite_48 @cite_27 , it is beneficial to use contour cues learned from human annotations. Then rather than treating human segmentations as only static samples, what other stuff can we learn? In @cite_46 , it is noticed that although human segmentations are of the high quality, there are surprisingly many boundary mistakes, even in the renown BSDS500 dataset, see Fig. .a-b. Easy to see, these mistakes are made intentionally instead of by casual operations.
@cite_46 @cite_48 @cite_27
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An intuition on human segmentation is that when a human is moving in a video, the video-context (e.g., appearance and motion clues) may potentially infer reasonable mask information for the whole human body. Inspired by this, based on popular deep convolutional neural networks (CNN), we explore a very-weakly supervised learning framework for human segmentation task, where only an imperfect human detector is available along with massive weakly-labeled YouTube videos. In our solution, the video-context guided human mask inference and CNN based segmentation network learning iterate to mutually enhance each other until no further improvement gains. In the first step, each video is decomposed into supervoxels by the unsupervised video segmentation. The superpixels within the supervoxels are then classified as human or non-human by graph optimization with unary energies from the imperfect human detection results and the predicted confidence maps by the CNN trained in the previous iteration. In the second step, the video-context derived human masks are used as direct labels to train CNN. Extensive experiments on the challenging PASCAL VOC 2012 semantic segmentation benchmark demonstrate that the proposed framework has already achieved superior results than all previous weakly-supervised methods with object class or bounding box annotations. In addition, by augmenting with the annotated masks from PASCAL VOC 2012, our method reaches a new state-of-the-art performance on the human segmentation task. We propose a shape-based, hierarchical part-template matching approach to simultaneous human detection and segmentation combining local part-based and global shape-template-based schemes. The approach relies on the key idea of matching a part-template tree to images hierarchically to detect humans and estimate their poses. For learning a generic human detector, a pose-adaptive feature computation scheme is developed based on a tree matching approach. Instead of traditional concatenation-style image location-based feature encoding, we extract features adaptively in the context of human poses and train a kernel-SVM classifier to separate human nonhuman patterns. Specifically, the features are collected in the local context of poses by tracing around the estimated shape boundaries. We also introduce an approach to multiple occluded human detection and segmentation based on an iterative occlusion compensation scheme. The output of our learned generic human detector can be used as an initial set of human hypotheses for the iterative optimization. We evaluate our approaches on three public pedestrian data sets (INRIA, MIT-CBCL, and USC-B) and two crowded sequences from Caviar Benchmark and Munich Airport data sets. Recent leading approaches to semantic segmentation rely on deep convolutional networks trained with human-annotated, pixel-level segmentation masks. Such pixel-accurate supervision demands expensive labeling effort and limits the performance of deep networks that usually benefit from more training data. In this paper, we propose a method that achieves competitive accuracy but only requires easily obtained bounding box annotations. The basic idea is to iterate between automatically generating region proposals and training convolutional networks. These two steps gradually recover segmentation masks for improving the networks, and vise versa. Our method, called "BoxSup", produces competitive results (e.g., 62.0 mAP for validation) supervised by boxes only, on par with strong baselines (e.g., 63.8 mAP) fully supervised by masks under the same setting. By leveraging a large amount of bounding boxes, BoxSup further yields state-of-the-art results on PASCAL VOC 2012 and PASCAL-CONTEXT [26]. Given that natural image segmentation is well-known as an ill-posed problem, then how can we design an algorithm to obtain good performance as human subjects? A choice is to learn from human segmentations as MCG [3] which obtains the state-of-the-art performance and fairly high computational efficiency. Then a question arises here: what should we learn? The way all existing literatures exploit human segmentations ignores a basic fact that human segmentations are produced by human operations. The human segmentation process would inevitably fulfill the human behavior’s principles including the least effort principle (LEP): a human will strive to solve his problem in such a way as to minimize the total work that he must expend [1]. The principle gives us a new insight into our problem. Suppose you are a human subject in the BSDS segmentation experiment, and are required to segment images into pieces under the ending instruction: each piece contains only one single distinguished thing. Then your total effort F of segmenting an image should including understanding images (U) to guide following operations, and tracing boundaries (T ) by hands with a mouse: F (I,S) =U (I)+T (S). Then from the viewpoint of LEP, human subjects would like to find an acceptable segmentation S by minimizing their effort, or formally,