Spatial Sparse Coding Based MIL Algorithm for Criminal Investigation Image Classification

被引：0

作者：

Li D.-X. ^{[1
,2
]}

Wu Q. ^{[1
]}

Qiu X. ^{[1
]}

Liu Y. ^{[1
,2
]}

机构：

[1] School of Communication and Information Technoogy, Xi'an University of Posts and Telecommunications, Xi'an

[2] Ministry of Public Security Key Laboratory of Electronic Information Application Technology for Scene Investigation, Xi'an

来源：

Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2019年 / 48卷 / 01期

关键词：

Criminal investigation image classification; Multi-instance learning; Spatial sparse coding; Support vector machine;

D O I：

10.3969/j.issn.1001-0548.2019.01.012

中图分类号：

学科分类号：

摘要：

Focusing on the classification problem of the criminal investigation, a multi-instance learning (MIL) algorithm based on spatial sparse coding (SSC) is proposed. By using the dense scale invariant feature transform (SIFT) principle, a multi-instance modeling scheme with instance position information is constructed to transform the problem of criminal investigation image classification into a multi-instance learning (MIL) problem. Based on the diversity density (DD) function and the sparse coding theory, a new dictionary construct method and spatially sparse coding (SSC) technique are designed for MIL, to extract the metadata for each multi-instance bag. At last, a new MIL algorithm called SSC-MIL is proposed by combining the large-scale linear support vector machine method. Experimental results on the 14 cases of real criminal investigation image show that the proposed method is effective, and the classification accuracy is higher than other methods. © 2019, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.

引用

页码：68 / 73

页数：5

共 18 条

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