Multiple kernel learning

About: Multiple kernel learning is a research topic. Over the lifetime, 1630 publications have been published within this topic receiving 56082 citations.

Reducing samples for accelerating multikernel semiparametric support vector regression

Abstract: In this paper, the reducing samples strategy instead of classical @n-support vector regression (@n-SVR), viz. single kernel @n-SVR, is utilized to select training samples for admissible functions so as to curtail the computational complexity. The proposed multikernel learning algorithm, namely reducing samples based multikernel semiparametric support vector regression (RS-MSSVR), has an advantage over the single kernel support vector regression (classical @e-SVR) in regression accuracy. Meantime, in comparison with multikernel semiparametric support vector regression (MSSVR), the algorithm is also favorable for computational complexity with the comparable generalization performance. Finally, the efficacy and feasibility of RS-MSSVR are corroborated by experiments on the synthetic and real-world benchmark data sets.

Learning to Rank for Blind Image Quality Assessment

Abstract: Blind image quality assessment (BIQA) aims to predict perceptual image quality scores without access to reference images. State-of-the-art BIQA methods typically require subjects to score a large number of images to train a robust model. However, subjective quality scores are imprecise, biased, and inconsistent, and it is challenging to obtain a large scale database, or to extend existing databases, because of the inconvenience of collecting images, training the subjects, conducting subjective experiments, and realigning human quality evaluations. To combat these limitations, this paper explores and exploits preference image pairs (PIPs) such as "the quality of image $I_a$ is better than that of image $I_b$" for training a robust BIQA model. The preference label, representing the relative quality of two images, is generally precise and consistent, and is not sensitive to image content, distortion type, or subject identity; such PIPs can be generated at very low cost. The proposed BIQA method is one of learning to rank. We first formulate the problem of learning the mapping from the image features to the preference label as one of classification. In particular, we investigate the utilization of a multiple kernel learning algorithm based on group lasso (MKLGL) to provide a solution. A simple but effective strategy to estimate perceptual image quality scores is then presented. Experiments show that the proposed BIQA method is highly effective and achieves comparable performance to state-of-the-art BIQA algorithms. Moreover, the proposed method can be easily extended to new distortion categories.

User Authentication Method Based on MKL for Keystroke and Mouse Behavioral Feature Fusion

Abstract: In order to improve the recognition rate of users with single behavioral feature and prevent impostors from restricting an input device to avoid detection, a dual-index user authentication method based on Multiple Kernel Learning (MKL) for keystroke and mouse behavioral feature fusion was proposed in this paper. Due to the heterogeneity between the keystroke features and the mouse features, we argue that each type of features is mapped to a suitable kernel and the weights of each kernel are obtained through computing and then summed to obtain a compound kernel that implements the multifeature fusion. The dataset used in this paper was collected under complete uncontrolled condition from some volunteers by using our data collection program. The experimental results show that the proposed method can obtain the best recognition accuracy of 89.6%. Compared to the traditional methods of single feature, the dual-index method can get more stable and effective authentication. Therefore, the proposed method in this paper fully demonstrates the reliability of dual-index user authentication.

A multiple kernel learning approach to multi-modal pedestrian classification

Abstract: Pedestrian detection is a key problem in many computer vision applications, especially in surveillance and security systems. To this end, information integration from different imaging modalities, such as thermal infrared and visible spectrum, can significantly improve the detection rate in respect to mono-modal strategies. For this reason, an effective fusion scheme is necessary to combine the information presented by multiple sensors. In this paper, we propose a pedestrian classification method based on the multiple kernel learning framework; standard pixel features (such as spatial derivatives) from both imaging modalities are employed to learn several feature-related basic kernels and a compound kernel is found as an optimized linear combination of basic kernels. Finally the compound kernel is used to train an SVM. Experiments performed on the OTCBVS dataset [1], demonstrate that our recipe definitely outclasses a wide set of literature fusion modalities.

Hyperspectral image classification method based on affinity propagation clustering and sparse multiple kernel learning

Abstract: The invention discloses a hyperspectral image classification method based on affinity propagation clustering and sparse multiple kernel learning to mainly solve a problem that technologies of the prior art are low in hyperspectral image classification performance. A technical solution is that training samples in all wave bands are used for constructing a kernel matrix set, an affinity propagation method is used for clustering, and a kernel matrix subset which is high in discriminability and low in redundancy is selected; by using the kernel matrix subset which is selected, kernel weight and support vector coefficients can be learned via a sparse-constrained multiple kernel learning method; unknown hyperspectral images can be classified via a learned classifier. According to the hyperspectral image classification method, the multiple kernel learning method is adopted, a plurality kinds of kernels that are different in function and parameter are used, complex hyperspectral data having changeable local distribution can be processed, high-precision hyperspectral image classification results can be obtained, and the hyperspectral image classification method can be applied to discrimination of surface features in the fields of agriculture monitoring, geological exploration, disaster environment assessment and the like.