Abstract
Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model. As a result, it is often very time-consuming and labor-intensive, yet still subject to manual errors and subjective inconsistency. To tackle such difficulties, a weakly supervised ISS approach is proposed, in which the challenging problem of label inference from image-level to pixel-level will be particularly addressed, using image patches and conditional random fields (CRF). An improved simple linear iterative cluster (SLIC) algorithm is employed to extract superpixels. for image segmentation. Specifically, it generates various numbers of superpixels according to different images, which can be used to guide the process of image patch extraction based on the image-level labeled information. Based on the extracted image patches, the CRF model is constructed for inferring semantic class labels, which uses the potential energy function to map from the image-level to pixel-level image labels. Finally, patch based CRF (PBCRF) model is used to accomplish the weakly supervised ISS. Experiments conducted on two publicly available benchmark datasets, MSRC and PASCAL VOC 2012, have demonstrated that our proposed algorithm can yield very promising results compared to quite a few state-of-the-art ISS methods, including some deep learning-based models.
Highlights
Different from conventional image segmentation, by combining image segmentation and object recognition, image semantic segmentation (ISS) divides an image into many image blocks to identify the semantic category of each block [1]
We propose an image patch and conditional random fields (CRF) based weakly supervised ISS algorithm (IPCRFWSS), which can successfully achieve semantic label inference and prediction; We propose an algorithm for automatic estimation of the recommended number of superpixels for different images, which has significantly improved the efficiency and accuracy of image segmentation as it can be used to generate image patches for image-level labels; A patch based CRF (PBCRF) model is introduced for semantic class inference from image-level to pixel-level labels
A novel PBCRF model is proposed for ISS with image-level labels, which provides an effective solution to the weakly supervised ISS problems
Summary
Different from conventional image segmentation, by combining image segmentation and object recognition, image semantic segmentation (ISS) divides an image into many image blocks to identify the semantic category of each block [1]. It has been widely applied in semantic information extraction from images for scene understanding and object recognition [2,3]. Supervised ISS requires pixel based labeling of the whole image, which is often achieved manually. Considering the difficulty of obtaining pixel-level labeling in fully supervised learning, weakly supervised ISS is more desirable as it does not require pixel based labeling of the whole images the associated labor cost and time consumption can be reduced significantly.
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