Weakly-supervised semantic segmentation aims to assign each pixel a semantic category under weak supervisions, such as image-level tags. Most of existing weakly-supervised semantic segmentation methods do not use any feedback from segmentation output and can be considered as open-loop systems. They are prone to accumulated errors because of the static seeds and the sensitive structure information. In this paper, we propose a generic self-adaptation mechanism for existing weakly-supervised semantic segmentation methods by introducing two feedback chains, thus constituting a closed-loop system. Specifically, the first chain iteratively produces dynamic seeds by incorporating cross-image structure information, whereas the second chain further expands seed regions by a customized random walk process to reconcile inner-image structure information characterized by superpixels. Experiments on PASCAL VOC 2012 suggest that our network outperforms state-of-the-art methods with significantly less computational and memory burden.
In this paper, we introduce two feedback chains into existing weakly-supervised semantic segmentation network to form a close-loop system, which enables iterative extraction of robust structure information and dynamical correction of errors made by inaccurate seed localization. Specifically, the first feedback chain updates the seed by the network output to recover the cross-image structure, whereas the second feedback chain incorporates robust inner-image structure information characterized by a relationship matrix built upon superpixels to further expand seed regions. It is worth noting that deep seed region growing (DSRG) @cite_17 also uses dynamic seed. However, it does not correct the inaccurate seed localization explicitly, which constrains the upper bound of segmentation performance. On the other hand, Mining Common Object Features (MCOF) @cite_25 also suggests using superpixels to robustly rebuild inner-image structure information in the presence of noisy labels. Unlike MCOF, we use a simple relationship matrix to characterize superpixels, rather than an extra network that requires significantly higher computational and memory burden. Moreover, experiments demonstrate that our network outperforms both DSRG and MCOF with a large margin.
@cite_25 @cite_17
2798715809 2799124825
This paper studies the problem of learning image semantic segmentation networks only using image-level labels as supervision, which is important since it can significantly reduce human annotation efforts. Recent state-of-the-art methods on this problem first infer the sparse and discriminative regions for each object class using a deep classification network, then train semantic a segmentation network using the discriminative regions as supervision. Inspired by the traditional image segmentation methods of seeded region growing, we propose to train a semantic segmentation network starting from the discriminative regions and progressively increase the pixel-level supervision using by seeded region growing. The seeded region growing module is integrated in a deep segmentation network and can benefit from deep features. Different from conventional deep networks which have fixed static labels, the proposed weakly-supervised network generates new labels using the contextual information within an image. The proposed method significantly outperforms the weakly-supervised semantic segmentation methods using static labels, and obtains the state-of-the-art performance, which are 63.2 mIoU score on the PASCAL VOC 2012 test set and 26.0 mIoU score on the COCO dataset. In diffusion-based saliency detection, an image is partitioned into superpixels and mapped to a graph, with superpixels as nodes and edge strengths proportional to superpixel similarity. Saliency information is then propagated over the graph using a diffusion process, whose equilibrium state yields the object saliency map. The optimal solution is the product of a propagation matrix and a saliency seed vector that contains a prior saliency assessment. This is obtained from either a bottom-up saliency detector or some heuristics. In this work, we propose a method to learn optimal seeds for object saliency. Two types of features are computed per superpixel: the bottom-up saliency of the superpixel region and a set of mid-level vision features informative of how likely the superpixel is to belong to an object. The combination of features that best discriminates between object and background saliency is then learned, using a large-margin formulation of the discriminant saliency principle. The propagation of the resulting saliency seeds, using a diffusion process, is finally shown to outperform the state of the art on a number of salient object detection datasets. Most existing superpixel algorithms ignore the spatial structure and regularity properties, which result in undesirable sizes and location relationships for the subsequent processing. In this paper, we introduce a new method to generate the regularity preserved superpixels. Starting from the lattice seeds, our method relocates them to the pixel with locally maximal edge magnitudes and treats them as the superpixel junctions. Then, the shortest path algorithm is employed to find the local optimal boundary connecting each adjacent junction pair. Thanks to the local constraints, our method obtains homogeneous superpixels with adjacency in lowly textured and uniform regions and simultaneously preserves the boundary adherence in the high contrast contents. Our method preserves the regularity property without significantly sacrificing the segmentation accuracy. Moreover, we extend this regular constraint for generating the supervoxels. Our method obtains the regular supervoxels, which preserves the structural relation on both spatial and temporal spaces of the video. Quantitative and qualitative experimental results on benchmark datasets demonstrate that our simple but effective method outperforms the existing regular superpixel methods. We present a novel image superpixel segmentation approach using the proposed lazy random walk (LRW) algorithm in this paper. Our method begins with initializing the seed positions and runs the LRW algorithm on the input image to obtain the probabilities of each pixel. Then, the boundaries of initial superpixels are obtained according to the probabilities and the commute time. The initial superpixels are iteratively optimized by the new energy function, which is defined on the commute time and the texture measurement. Our LRW algorithm with self-loops has the merits of segmenting the weak boundaries and complicated texture regions very well by the new global probability maps and the commute time strategy. The performance of superpixel is improved by relocating the center positions of superpixels and dividing the large superpixels into small ones with the proposed optimization algorithm. The experimental results have demonstrated that our method achieves better performance than previous superpixel approaches.