An oil painters recognition method based on cluster multiple kernel learning algorithm

A lot of image processing research works focus on natural images, such as in classification, clustering, and the research on the recognition of artworks (such as oil paintings), from feature extraction to classifier design, is relatively few. This paper focuses on oil painter recognition and tries to find the mobile application to recognize the painter. This paper proposes a cluster multiple kernel learning algorithm, which extracts oil painting features from three aspects: color, texture, and spatial layout, and generates multiple candidate kernels with different kernel functions. With the results of clustering numerous candidate kernels, we selected the sub-kernels with better classification performance, and use the traditional multiple kernel learning algorithm to carry out the multi-feature fusion classification. The algorithm achieves a better result on the Painting91 than using traditional multiple kernel learning directly

Soliton solution of the osmosis K(2, 2) equation

In this Letter, by using the bifurcation method of dynamical systems, we obtain the analytic expressions of soliton solution of the osmosis K(2, 2) equation.Comment: 8 page

Balanced geological section for extensional tectonic basin and its implication: An example from southern Songliao Basin

挤压构造的平衡地质剖面分析已经广泛应用于造山带构造分析, 但伸展构造区的平衡地质剖面分析实例仍然很少. 运用盆地分析的技术与方法, 分层序或阶段将地质构造依次恢复、地层逐层回剥, 并通过在松辽盆地南部吉林两井油田扶余油层4 条剖面的实践, 复原出不同时代盆地构造与地层发育的连续剖面, 揭示出松辽盆地南部主要构造样式是以浅表构造层次的负花状构造及深层剥离断层发育为特征; 断层生长指数、盆地的伸展史和伸展量等参数显示, 晚白垩世是构造转型的重要阶段, 此前主要为走滑构造样式形成阶段, 此后则主要为伸展滑脱构造发育阶段. 在此基础上, 提出松辽盆地具有伸展- 走滑双重力学构造性质, 可能是一个弧后构造盆地.The balanced geological sect ion has been widely used for the analysis of orogenic belt, but it is infrequent for ex tensional basins. In this paper, 4 extensional balanced geological sect ion analysis were practiced in Fuyu oil layer of Liangjing , Jilin oilfield, southern Songliao basin with the technology and method, including deformation history restoration, decompaction and erosion restoration. The structure of different ages and the continuous sedimentary sections have been restored. T he result s show that the structural styles possess the characteristics of negative flower structure in the shallow level and ex tensional detachment in deep level. The parameters, including fault growth index, the basin ex tensional history and fault detachment depth, indicate that Late Cretaceous is an important stage for the structure transferring mainly with a strike-slip style before this time and an ex tensional structure and detachment after this time. Therefore, a basin model with twin dynamic property and back-arc characters is proposed.published_or_final_versio

A Learned Index for Exact Similarity Search in Metric Spaces

Indexing is an effective way to support efficient query processing in large databases. Recently the concept of learned index has been explored actively to replace or supplement traditional index structures with machine learning models to reduce storage and search costs. However, accurate and efficient similarity query processing in high-dimensional metric spaces remains to be an open challenge. In this paper, a novel indexing approach called LIMS is proposed to use data clustering and pivot-based data transformation techniques to build learned indexes for efficient similarity query processing in metric spaces. The underlying data is partitioned into clusters such that each cluster follows a relatively uniform data distribution. Data redistribution is achieved by utilizing a small number of pivots for each cluster. Similar data are mapped into compact regions and the mapped values are totally ordinal. Machine learning models are developed to approximate the position of each data record on the disk. Efficient algorithms are designed for processing range queries and nearest neighbor queries based on LIMS, and for index maintenance with dynamic updates. Extensive experiments on real-world and synthetic datasets demonstrate the superiority of LIMS compared with traditional indexes and state-of-the-art learned indexes.Comment: 14 pages, 14 figures, submitted to Transactions on Knowledge and Data Engineerin

RAEDiff: Denoising Diffusion Probabilistic Models Based Reversible Adversarial Examples Self-Generation and Self-Recovery

Collected and annotated datasets, which are obtained through extensive efforts, are effective for training Deep Neural Network (DNN) models. However, these datasets are susceptible to be misused by unauthorized users, resulting in infringement of Intellectual Property (IP) rights owned by the dataset creators. Reversible Adversarial Exsamples (RAE) can help to solve the issues of IP protection for datasets. RAEs are adversarial perturbed images that can be restored to the original. As a cutting-edge approach, RAE scheme can serve the purposes of preventing unauthorized users from engaging in malicious model training, as well as ensuring the legitimate usage of authorized users. Nevertheless, in the existing work, RAEs still rely on the embedded auxiliary information for restoration, which may compromise their adversarial abilities. In this paper, a novel self-generation and self-recovery method, named as RAEDiff, is introduced for generating RAEs based on a Denoising Diffusion Probabilistic Models (DDPM). It diffuses datasets into a Biased Gaussian Distribution (BGD) and utilizes the prior knowledge of the DDPM for generating and recovering RAEs. The experimental results demonstrate that RAEDiff effectively self-generates adversarial perturbations for DNN models, including Artificial Intelligence Generated Content (AIGC) models, while also exhibiting significant self-recovery capabilities