Unsupervised Multi-Class Co-Segmentation via Joint-Cut Over $L_{1}$ -Manifold Hyper-Graph of Discriminative Image Regions

Image Virtual Viewpoint Generation Method under Hole Pixel Information Update

A virtual viewpoint generation method is proposed to address the problem of low fidelity in the generation of virtual viewpoints for images with overlapping pixel points. Virtual viewpoint generation factors such as overlaps, holes, cracks, and artifacts are analyzed and preprocessed. When the background of the hole is a simple texture, pheromone information around the hole is used as the support, a pixel at the edge of the hole is detected, and the hole is predicted at the same time, so that the hole area is filled in blocks. When the hole background has a relatively complex texture, the depth information of the hole pixels is updated with the inverse 3D transformation method, and the updated area pheromone is projected onto the auxiliary plane and compared with the known plane pixel auxiliary parameters. The hole filling is performed according to the symmetry of the pixel position of the auxiliary reference viewpoint plane to obtain the virtual viewpoint after optimization. The proposed method was validated using image quality metrics and objective evaluation metrics such as PSNR. The experimental results show that the proposed method could generate virtual viewpoints with high fidelity, excellent quality, and a short image-processing time, which effectively enhanced the virtual viewpoint generation performance.

A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

Forests provide various important things to human life. Fire is one of the main disasters in the world. Nowadays, the forest fire incidences endanger the ecosystem and destroy the native flora and fauna. This affects individual life, community and wildlife. Thus, it is essential to monitor and protect the forests and their assets. Nowadays, image processing outputs a lot of required information and measures for the implementation of advanced forest fire-fighting strategies. This work addresses a new color image segmentation method based on principal component analysis (PCA) and Gabor filter responses. Our method introduces a new superpixels extraction strategy that takes full account of two objectives: regional consistency and robustness to added noises. The novel approach is tested on various color images. Extensive experiments show that our method obviously outperforms existing segmentation variants on real and synthetic images of fire forest scenes, and also achieves outstanding performance on other popular benchmarked images (e.g., BSDS, MRSC). The merits of our proposed approach are that it is not sensitive to added noises and that the segmentation performance is higher with images of nonhomogeneous regions.

Multi-View Saliency-Guided Clustering for Image Cosegmentation

Image cosegmentation aims at extracting the common objects from multiple images simultaneously. Existing methods mainly solve cosegmentation via the pre-defined graph, which lacks flexibility and robustness to handle various visual patterns. Besides, similar backgrounds also confuse the identifying of the common foreground. To address these issues, we propose a novel Multi-view Saliency-Guided Clustering algorithm (MvSGC) for the image cosegmentation task. In our model, the unsupervised saliency prior is used as partition-level side information to guide the foreground clustering process. To achieve robustness to noises and missing observations, similarities on instance-level and partition-level are both considered. Specifically, a unified clustering model with cosine similarity is proposed to capture the intrinsic structure of data and keep partition result consistent with the side information. Moreover, we leverage multi-view weight learning to integrate multiple feature representations to further improve the robustness of our approach. A K-means-like optimization algorithm is developed to proceed the constrained clustering in a highly efficient way with theoretical support. Experimental results on three benchmark datasets (i.e., the iCoseg, MSRC and Internet image dataset) and one RGB-D image dataset demonstrate the superiority of applying our clustering method for image cosegmentation.