Abstract
Crime Scene Investigation images have many semantic categories and complex image contents. The Convolution Neural Network (CNN) feature cannot express the uniformity of image content and high dimensional features can lead to redundancy of feature vectors in CNN. In the circumstance it is difficult to use CNN to process crime scene investigation images. To solve the above problems, we propose a fusion CNN algorithm based on feature selection for the classification of crime scene investigation images. In this paper, we build the fusion CNN features to enhance the ability of representation by fusing the convolutional layer with the fully connected layer. Then we select the fusion features with Laplacian score and label mutual information. Finally, we use the obtained features to train Support Vector Machine (SVM) classifier on the Crime Scene Investigation Images Database (CSID). Experiments show that the average classification accuracy of the proposed method can reach 93.67%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu, Y., Hu, D., Fan, J.L.: A survey of crime scene investigation image retrieval. Acta Electronica Sinica 46(3), 761–768 (2018)
Zhao, Y.D., Wang, Q., Fan, J.L.: Fuzzy KNN classification for criminal investigation image scene. Appl. Res. Comput. 31(10), 3158–3160 (2014)
Liu, Y., Yan, H., Lim, K.P.: Study on rotation-invariant texture feature extraction for tire pattern retrieval. Multidimension. Syst. Signal Process. 28(2), 757–770 (2017)
Bai, X.J., Shi, T.Y., Liu, Y.: An improved criminal scene investigation image classification algorithm. J. Xi’an Univ. Posts Telecommun. 21(6), 24–28 (2016)
Lan, R., Guo, S.C., Jia, S.Y.: Forensic image retrieval algorithm based on fusion. Comput. Eng. Des. 39(4), 1106–1110 (2018)
Liu, Y., Hu, D., Fan, J.L., et al.: Multi-feature fusion for crime scene investigation image retrieval. In: 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–7. IEEE (2017)
Liu, Y., Wang, F.-P., Hu, D., et al.: Multi-feature fusion with SVM classification for crime scene investigation image retrieval. In: IEEE International Conference on Signal & Image Processing, pp. 160–165. IEEE (2017)
Ezeobiejesi, J., Bhanu, B.: Latent fingerprint image quality assessment using deep learning. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 621–630. IEEE Computer Society (2018)
Vagac, M., Povinsky, M., Melichercik, M.: Detection of shoe sole features using DNN. In: 2017 IEEE 14th International Scientific Conference on Informatics, pp. 416–419. IEEE (2017)
Liu, Y., Peng, Y.N., et al.: A novel image retrieval algorithm based on transfer learning and fusion features. World Wide Web 22(3), 1313–1324 (2018)
Kulkarni, P., Zepeda, J., Jurie, F., et al.: Hybrid multi-layer deep CNN/aggregator feature for image classification. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1379–1383. IEEE (2015)
Cai, D., Zhang, C., He, X.: Unsupervised feature selection for multi-cluster data. In: ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 333–342 (2010)
Acknowledgments
This work was supported by Project of International Science and Technology Cooperation and Exchange in Shaanxi Province of China (2018KW-003), National Natural Science Fund of China (61802305), and the graduate innovation fund project of Xi’an University of Posts and Telecommunications (CXJJLI2018012).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Q., Liu, Y., Wang, F., Lu, J., Li, D. (2020). Fusion CNN Based on Feature Selection for Crime Scene Investigation Image Classification. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_83
Download citation
DOI: https://doi.org/10.1007/978-3-030-32591-6_83
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32590-9
Online ISBN: 978-3-030-32591-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)