Efficient object-based surveillance image search using spatial pooling of convolutional features

Journal of Visual Communication and Image Representation

Volumn 45, Issue , 2017, Pages 62-76

  Ahmad, Jamil ^a   Mehmood, Irfan ^a   Baik, Sung Wook ^a  

^a Sejong University   (South Korea)

Author keywords

Convolutional features; Features extraction; Surveillance image search; Surveillance network

Indexed keywords

CONVOLUTION; DIGITAL STORAGE; EXTRACTION; IMAGE PROCESSING; IMAGE RETRIEVAL; NEURAL NETWORKS; SENSOR NODES;

COMPUTATIONAL SENSORS; CONVOLUTIONAL FEATURES; CONVOLUTIONAL NEURAL NETWORK; FEATURES EXTRACTION; IMAGE SEARCH; LOW DIMENSIONAL; SALIENT OBJECTS; SURVEILLANCE NETWORKS;

MONITORING;

EID: 85015422660     PISSN: 10473203     EISSN: 10959076     Source Type: Journal    
DOI: 10.1016/j.jvcir.2017.02.010     Document Type: Article

References (29)

1. [1] Kim, I.S., et al. Intelligent visual surveillance—a survey. Int. J. Control Autom. Syst. 8:5 (2010), 926–939.
2. [2] Maia, G., et al. A distributed data storage protocol for heterogeneous wireless sensor networks with mobile sinks. Ad Hoc Netw. 11:5 (2013), 1588–1602.
3. [3] Mehmood, I., et al. Saliency-directed prioritization of visual data in wireless surveillance networks. Inform. Fusion 24 (2015), 16–30.
4. [4] Ahmad, J., et al. Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems. J. Real-Time Image Proc., 2016, 1–17.
5. [5] Yan, T., Ganesan, D., Manmatha, R., Distributed image search in camera sensor networks. Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, 2008, ACM.
6. [6] Lowe, D.G., Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60:2 (2004), 91–110.
7. [7] Milovanovic, S., Stojmenovic, M., Quorum based image retrieval in large scale visual sensor networks. Ad-Hoc, Mobile, and Wireless Networks, 2012, Springer, 449–458.
8. [8] Sulić, V., et al. Efficient feature distribution for object matching in visual-sensor networks. Circ. Syst. Video Technol., IEEE Trans. 21:7 (2011), 903–916.
9. [9] Chatzichristofis, S.A., Boutalis, Y.S., CEDD: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval. Gasteratos, A., Vincze, M., Tsotsos, J.K., (eds.) Computer Vision Systems, 2008, Springer, 312–322.
10. [10] Liu, G.-H., et al. Image retrieval based on micro-structure descriptor. Pattern Recogn. 44:9 (2011), 2123–2133.
11. [11] Liu, G.-H., et al. Image retrieval based on multi-texton histogram. Pattern Recogn. 43:7 (2010), 2380–2389.
12. [12] Liu, G.-H., Yang, J.-Y., Content-based image retrieval using color difference histogram. Pattern Recogn. 46:1 (2013), 188–198.
13. [13] Wang, X., Wang, Z., A novel method for image retrieval based on structure elements’ descriptor. J. Vis. Commun. Image Rep. 24:1 (2013), 63–74.
14. [14] Zhao, M., Zhang, H., Sun, J., A novel image retrieval method based on multi-trend structure descriptor. J. Vis. Commun. Image Rep. 38 (2016), 73–81.
15. [15] Ahmad, J., et al. Multi-scale local structure patterns histogram for describing visual contents in social image retrieval systems. Multimedia Tools Appl. 75:20 (2016), 12669–12692.
16. [16] Halgamuge, M.N., et al. An estimation of sensor energy consumption. Prog. Electromagn. Res. B, 2009.
17. [17] Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 2012, Curran Associates Inc.
18. [18] Deng, J., et al. Imagenet: a large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2009, 2009, IEEE.
19. [19] Farabet, C., et al. Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35:8 (2013), 1915–1929.
20. [20] Liu, L., Shen, C., van den Hengel, A., The treasure beneath convolutional layers: cross-convolutional-layer pooling for image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
21. [21] Baltieri, D., Vezzani, R., Cucchiara, R., 3dpes: 3d people dataset for surveillance and forensics. Proceedings of the 2011 Joint ACM Workshop on Human Gesture and Behavior Understanding, 2011, ACM.
22. [22] Gray, D., Brennan, S., Tao, H., Evaluating appearance models for recognition, reacquisition, and tracking. Proc. IEEE International Workshop on Performance Evaluation for Tracking and Surveillance (PETS), 2007, Citeseer.
23. [23] Ozuysal, M., Lepetit, V., Fua, P., Pose estimation for category specific multiview object localization. IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2009, 2009, IEEE.
24. [24] Yang, L., et al. A large-scale car dataset for fine-grained categorization and verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
25. [25] Arróspide, J., Salgado, L., Nieto, M., Video analysis-based vehicle detection and tracking using an MCMC sampling framework. EURASIP J. Adv. Signal Process. 2012:1 (2012), 1–20.
26. [26] Glasner, D., et al. Viewpoint-aware object detection and continuous pose estimation. Image Vis. Comput. 30:12 (2012), 923–933.
27. [27] Schwartz, W.R., Davis, L.S., Learning discriminative appearance-based models using partial least squares. 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing, 2009, IEEE.
28. [28] Cheng, D.S., et al. Custom pictorial structures for re-identification. BMVC, 2011.
29. [29] Li, W., Wang, X., Locally aligned feature transforms across views. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013.