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In challenging scenarios (e.g., small objects and cluttered backgrounds), most existing algorithms suffer from inconsistent results with human visual attention. Since fixation prediction can better model the human visual attention mechanism and has a strong correlation with salient objects. Inspired by this, we proposed a fixation guidance network (FGNet) for salient object detection, which innovatively used fixation prediction to guide both salient object detection and edge detection. Firstly, a multi-branch network structure was designed to achieve multi-task detection. Each branch unit significantly learned the extracted features to accomplish the correct prediction. Secondly, given the strong correlation between the fixation and salient objects, a fixation guidance module was employed to guide salient object detection and edge detection for obtaining more accurate detection results. Finally, to full use the complementary relationship between salient features and edge features, we proposed a multi-resolution feature interaction module to achieve mutual optimization within the same feature and between the different features for suppressing noise and enhancing their representations. The experimental results show that our proposed method performed better in challenging scenes and outperformed existing state-of-the-art algorithms in several metrics on four public benchmark datasets. [ABSTRACT FROM AUTHOR]

This document is a retraction note for an article titled "Editorial: neural computing in next-generation virtual reality technology" published in the journal Neural Computing & Applications. The article has been retracted by the Editor-in-Chief and the publisher due to concerns regarding compromised editorial handling and peer review process, inappropriate or irrelevant references, and not being in scope of the journal or guest-edited issue. The retraction note mentions that one of the authors, Zhihan Lv, did not explicitly state whether they agree with the retraction, Xiaonan Luo did not respond to correspondence about the retraction, and the publisher was unable to obtain a current contact email for Jim Jingyan Wang. The publisher, Springer Nature, remains neutral regarding jurisdictional claims and institutional affiliations. [Extracted from the article]

The article titled "Retraction Note: A novel approach to automatic detection of presentation slides in educational videos" has been retracted by the Editor-in-Chief and the publisher due to concerns regarding compromised editorial handling and peer review process, inappropriate references, and not being in scope of the journal or guest-edited issue. The authors of the article disagree with this retraction. The publisher, Springer Nature, remains neutral in regards to jurisdictional claims and institutional affiliations. [Extracted from the article]

Local descriptors play a crucial role in numerous computer vision and pattern recognition applications. This paper proposes a novel local descriptor, called the quaternionic local angular binary pattern (QLABP), for color image classification. QLABP is based on the quaternionic representation (QR) of color images such that it is able to handle all color components holistically as well as consider their relations. Using QR, the quaternionic angular information is further developed to account for more color characteristics. We provide two ways to derive the quaternionic angular information from different perspectives. A pattern encoding operation is finally conducted on the obtained angular information to obtain QLABP. The effectiveness of QLABP has successfully been evaluated by comparing with several state-of-the-art descriptors. [ABSTRACT FROM AUTHOR]

Spectral clustering-based subspace clustering methods have attracted broad interest in recent years. This kind of methods usually uses the self-representation in the original space to extract the affinity between the data points. However, we can usually find a subspace where the affinity of the projected data points can be extracted by self-representation more effectively. Moreover, only using the self-representation in the original space cannot handle nonlinear manifold clustering well. In this paper, we present robust subspace learning-based low-rank representation learning a subspace favoring the affinity extraction for the low-rank representation. The process of learning the subspace and yielding the representation is conducted simultaneously, and thus, they can benefit from each other. After extending the linear projection to nonlinear mapping, our method can handle manifold clustering problem which can be viewed as a general case of subspace clustering. In addition, the ℓ 2 , 1 -norm used in our model can increase the robustness of our method. Extensive experimental results demonstrate the effectiveness of our method on manifold clustering. [ABSTRACT FROM AUTHOR]

Recent advancement in learning and teaching methodology experimented with virtual reality (VR)-based presentation form to create immersive learning and training environment. The quality of such educational VR applications not only relies on the virtual model, but the 2D presentation materials such as text, diagrams and figures. However, manual designing or seeking these educational resources is both labor intensive and time-consuming. In this paper, we introduce a new automatic algorithm to detect and extract presentation slides in educational videos, which will provide abundant resources for creating slide-based immersive presentation environment. The proposed approach mainly involves five core components: shot boundary detection, training instances collection, shot classification, slide region detection and slide transition detection. We conducted comparison experiment to evaluate the performance of the proposed method. The results indicate that, in comparison with peer method, the proposed method improves the precision of slide detection from 81.6 to 92.6% and recall from 74.7 to 86.3% on average. With the detected slides, content analyzer can be employed to further extract reusable elements, which can be used for developing VR-based educational applications. [ABSTRACT FROM AUTHOR]