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.

Fraunhofer FIRST's Submission to ImageCLEF2009 Photo Annotation Task: Non-sparse Multiple Kernel Learning.

Abstract: In order to achieve good performance in image annotation tasks, it is necessary to combine information from various image features. In our submission, we applied the nonsparse multiple kernel learning for feature combination proposed by Kloft et al.(2009) to the ImageCLEF2009 photo annotation data. Since some of the concepts of the ImageCLEF task are rather abstract, we conjectured that color histograms are informative for some categories such as sky and snow. Therefore we tried pyramid histograms of pixel colors. Since the images are not aligned, we sorted histograms at different places, when computing similarity of two images. Short description of our methods will be presented and obtained results will be discussed in this manuscript.

Medical image classification with multiple kernel learning

Abstract: Nowadays, medical images are generated by hospitals and medical centers rapidly. The large volume of medical image data produces a strong need to effective medical image retrieval. The visual characteristic of medical image, such as modality, anatomical region etc., are important information and can be used to improve the retrieval process. Even though some of the information is contained in the DICOM headers, it has been reported that DICOM headers contain a relatively high rate of errors. And for on-line medical collection, these metadata can be lost when medical images are compressed. In this paper, we propose an algorithm for medical image classification according to their visual content. Our method uses multiple kernel learning (MKL) to combine different visual features, and learn the optimal mixing weights for each class adaptively. This method is evaluated on a medical image dataset with 1400 images, and the experimental results demonstrate the effectiveness of our method.

New Banknote Number Recognition Algorithm Based on Support Vector Machine

Abstract: Detecting the banknote serial number is an important task in business transaction. In this paper, we propose a new banknote number recognition method. The preprocessing of each banknote image is used to locate position of the banknote number image. Each number image is divided into non-overlapping partitions and the average gray value of each partition is used as feature vector for recognition. The optimal kernel function is obtained by the semi-definite programming (SDP). The experimental results show that the proposed method outperforms MASK, BP, HMM, Single SVM classifiers.

Structural feature selection for connectivity network-based MCI diagnosis

Abstract: Connectivity networks have been recently used for classification of neurodegenerative diseases, e.g., mild cognitive impairment (MCI). In typical connectivity network-based classification, features are often extracted from (multiple) connectivity networks and concatenated into a long vector for subsequent feature selection and classification. However, some useful network topological information may be lost in this type of approach. In this paper, we propose a new structural feature selection method which embeds the topological information of connectivity networks through graph kernel and then uses recursive feature elimination with graph kernel (RFE-GK) to select the most discriminative features. Furthermore, multiple kernel learning (MKL) is also adopted to combine multiple graph kernels for joint structural feature selectionfrom multiple connectivity networks. The experimental results show the efficacy of our proposed method with comparison to the state-of-the-art method in MCI classification, based on the connectivity networks.

Aviation Data Mining

Abstract: We explore dierent methods of data mining in the eld of aviation and their eectiveness. The