The task of semantic segmentation is to infer a predefined category label for each pixel in the image. For most cases, image segmentation is established as a fully supervised task. These methods all built on the basis of having access to sufficient pixel-wise annotated samples for training. However, obtaining the satisfied ground truth is not only labor intensive but also time-consuming, which severely hinders the generality of these fully supervised methods. Instead of pixel-level ground truth, weakly supervised approaches learn their models from much less prior information, e.g., image-level annotation. In this paper, we propose a novel conditional random field (CRF) based framework for weakly supervised semantic segmentation. Enlightened by jigsaw puzzles, we start the approach with merging superpixels from an image into larger pieces by a newly designed strategy. Then pieces from all the training images are gathered and associated with appropriate semantic labels by CRF. Thus, the piece library is constructed, achieving remarkable universality and flexibility. In the case of testing, we compare the superpixels with image pieces in the library and assign them the labels that minimize the potential energy. In addition, the proposed framework is fit for domain adaption and obtains promising results, which is of great practical value. Extensive experimental results on PASCAL VOC 2007, MSRC-21, and VOC 2012 databases demonstrate that our framework outperforms or is comparable to state-of-the-art segmentation methods.

Semantic image segmentation can be accomplished by assigning a proper object category label to each meaningful region of an image. Beyond the original bottom-up models, the use of top-down categorization information has been applied to semantic segmentation to improve performance. An excellent example of such a top-down scheme is to integrate a Conditional Random Field (CRF) model with sparse dictionary learning. However, the existing solutions merely consider the discrimination of dictionaries to obtain better sparse codes, without considering the inherent data locality characteristics. In this paper, we explore such characteristics and propose a novel semantic segmentation framework based on an innovative CRF model with locality-consistent dictionary learning. In particular, we propose two new locality-consistent dictionary learning strategies by capturing the local consistencies in the feature space and the label space. In addition, we develop a joint dictionary and a CRF model parameter learning algorithm to seamlessly integrate the proposed locality-consistent dictionary learning strategies into the CRF model. Extensive experiments are conducted with two popular databases of different traits (i.e., Graz-02 and PASCAL-CONTEXT). The simulation results confirm the efficiency of the proposed scheme, especially when training data is limited.

Cross-spectral recognition is still an open challenge in iris recognition. In cross-spectral iris recognition, there exist distinct device-specific bands between near-infrared (NIR) and visible (VIS) images, resulting in the distribution gap between samples from different spectra and thus severe degradation in recognition performance. To tackle this problem, we propose a new cross-spectral iris recognition method to learn spectral-invariant features by estimating device-specific bands. In the proposed method, G abor T rident N etwork (GTN) first utilizes the Gabor function’s priors to perceive iris textures under different spectra, and then codes the device-specific band as the residual component to assist the generation of spectral-invariant features. By investigating the device-specific band, GTN effectively reduces the impact of device-specific bands on identity features. Besides, we make three efforts to further reduce the distribution gap. First, S pectral A dversarial N etwork (SAN) adopts a class-level adversarial strategy to align feature distributions. Second, S ample- A nchor (SA) loss upgrades triplet loss by pulling samples to their class center and pushing away from other class centers. Third, we develop a higher-order alignment loss to measures the distribution gap according to space bases and distribution shapes. Extensive experiments on five iris datasets demonstrate the efficacy of our proposed method for cross-spectral iris recognition.