Crime Scene Investigation Unit of Shaanxi Province

Fengcheng, China

Crime Scene Investigation Unit of Shaanxi Province

Fengcheng, China
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Li D.,University of Posts and Telecommunications | Li D.,Crime Scene Investigation Unit of Shaanxi Province | Wang J.,University of Huddersfield | Zhao X.,University of Posts and Telecommunications | And 4 more authors.
Journal of Visual Communication and Image Representation | Year: 2014

In this paper, a novel multi-instance learning (MIL) algorithm based on multiple-kernels (MK) framework has been proposed for image classification. This newly developed algorithm defines each image as a bag, and the low-level visual features extracted from its segmented regions as instances. This algorithm is started from constructing a "word-space" from instances based on a collection of "visual-words" generated by affinity propagation (AP) clustering method. After calculating the distance between a "visual- word" and the bag (image), a nonlinear mapping mechanism is introduced for registering each bag as a coordinate point in the "word-space". In this case, the MIL problem is transformed into a standard supervised learning problem, which allows multiple-kernels support vector machine (MKSVM) classifiers to be trained for the image categorization. Compared with many popular MIL algorithms, the proposed method, named as MKSVM-MIL, shows its satisfactorily experimental results on the COREL dataset, which highlights the robustness and effectiveness for image classification applications. © 2014 Elsevier Inc. All rights reserved.


Li D.-X.,Xian University of Posts and Telecommunications | Li D.-X.,Crime Scene Investigation Unit of Shaanxi Province | Zhao X.-Q.,Xian University of Posts and Telecommunications | Li N.,Xian University of Posts and Telecommunications | Li N.,Crime Scene Investigation Unit of Shaanxi Province
Kongzhi yu Juece/Control and Decision | Year: 2013

Multi-instance learning(MIL) has been recognized as the fourth machine learning framework, and has been widely used in the image semantic analysis. Firstly, the concepts such as development history, characteristics and many useful testing datasets of MIL techniques are reviewed. Then, many popular MIL algorithms are also introduced in detail by using realworld applications based on image semantic analysis. Meanwhile, based on their machine learning mechanisms, related MIL algorithms are divided into a variety of categories, which highlights the processes and dominant features of different MIL algorithms. Finally, the trends and possible outputs for further researches are discussed in details.


Li D.,University of Posts and Telecommunications | Li D.,Crime Scene Investigation Unit of Shaanxi Province | Wang J.,University of Huddersfield | Liu Y.,University of Posts and Telecommunications | And 2 more authors.
International Journal of Pattern Recognition and Artificial Intelligence | Year: 2014

In this paper, a novel multi-instance learning (MIL) algorithm based on pyramid match kernel (PMK) and classifier ensemble is proposed for recognizing pornographic scene from image database. First, an improved JSEG image segmentation technique is deployed for dividing every image into several regions, and regards the whole image as a bag, the low-level visual features (i.e. color and texture) of each segmented region as instance. As a result, the pornographic images filtering problem can be transferred into a typical MIL problem. Second, similarity between the multi-instance bags is measured by PMK method, which allows MIL problem to be solved directly by the support vector machine (SVM). Finally, many base classifiers based on PMK with different levels are constructed, and the performance weighting rule is used to dynamically determine the weights of them, so the strategy of classifier ensemble is used to improve the filtering accuracy. In a real condition image set that the ratio of normal image to pornographic image is 9:1, experimental results show that the proposed algorithm, named PMKCE-MIL, is robust, and its performance is superior to other algorithms. © 2014 World Scientific Publishing Company.

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