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Multiple-instance learning with pairwise instance similarity

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Yuan L., Liu J., Tang X.

International Journal of Applied Mathematics and Computer Science

2014

tom 24

nr 3

567-577

Multiple-Instance Learning (MIL) has attracted much attention of the machine learning community in recent years and many real-world applications have been successfully formulated as MIL problems. Over the past few years, several Instance Selection-based MIL (ISMIL) algorithms have been presented by using the concept of the embedding space. Although they delivered very promising performance, they often require long computation times for instance selection, leading to a low efficiency of the whole learning process. In this paper, we propose a simple and efficient ISMIL algorithm based on the similarity of pairwise instances within a bag. The basic idea is selecting from every training bag a pair of the most similar instances as instance prototypes and then mapping training bags into the embedding space that is constructed from all the instance prototypes. Thus, the MIL problem can be solved with the standard supervised learning techniques, such as support vector machines. Experiments show that the proposed algorithm is more efficient than its competitors and highly comparable with them in terms of classification accuracy. Moreover, the testing of noise sensitivity demonstrates that our MIL algorithm is very robust to labeling noise.

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Abnormal prediction of dense crowd videos by a purpose-driven lattice Boltzmann model

100%

Xue Y., Liu P., Tao Y., Tang X.

International Journal of Applied Mathematics and Computer Science

2017

tom 27

nr 1

181-194

In the field of intelligent crowd video analysis, the prediction of abnormal events in dense crowds is a well-known and challenging problem. By analysing crowd particle collisions and characteristics of individuals in a crowd to follow the general trend of motion, a purpose-driven lattice Boltzmann model (LBM) is proposed. The collision effect in the proposed method is measured according to the variation in crowd particle numbers in the image nodes; characteristics of the crowd following a general trend are incorporated by adjusting the particle directions. The model predicts dense crowd abnormal events in different intervals through iterations of simultaneous streaming and collision steps. Few initial frames of a video are needed to initialize the proposed model and no training procedure is required. Experimental results show that our purpose-driven LBM performs better than most state-of-the-art methods.

Ograniczanie wyników

2 International Journal of Applied Mathematics and Computer Science

2 Tang X.

1 Liu J.

1 Liu P.

1 Tao Y.

1 Xue Y.

1 Yuan L.

1 2017

1 2014

Wyniki wyszukiwania

Multiple-instance learning with pairwise instance similarity

Abnormal prediction of dense crowd videos by a purpose-driven lattice Boltzmann model