Abstract
Efficient image retrieval and recognition are pivotal for optimal mobile web vision services. Traditional web-based solutions offer limited accuracy, high overhead, and struggle with vast image volumes. Transferring images for real-time cloud recognition demands stable communication, and large-scale concurrent requests strain computational and network resources. This paper introduces a distributed recognition approach, leveraging cloud-edge-device collaboration through edge computing’s low latency and high bandwidth. We present a lightweight image saliency detection model tailored for mobile web, enhancing initial image feature extraction. Additionally, we introduce an edge-based, deep learning-driven method to amplify image retrieval speed and precision. We incorporate a location and popularity-based caching system to alleviate strains on cloud resources and network bandwidth during extensive image requests. Our real-world tests validate our approach: our saliency detection model outpaces the benchmark by reducing the model size by up to 94%, making it suitable for mobile web deployment. The proposed method improves retrieval accuracy by 40% over cloud-based counterparts and cuts response latency by over 60%.
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References
Qiao, X., Ren, P., Dustdar, S., Liu, L., Ma, H., Chen, J.: Web ar: a promising future for mobile augmented reality-state of the art, challenges, and insights. Proc. IEEE 107(4), 651–666 (2019)
Qiao, X., Ren, P., Nan, G., Liu, L., Dustdar, S., Chen, J.: Mobile web augmented reality in 5g and beyond: Challenges, opportunities, and future directions. China Commun. 16(9), 141–154 (2019)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91–110 (2004)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: an efficient alternative to sift or surf. In: 2011 International Conference on Computer Vision, pp. 2564–2571. IEEE (2011)
Alcantarilla, P.F., Solutions, T.: Fast explicit diffusion for accelerated features in nonlinear scale spaces. IEEE Trans. Patt. Anal. Mach. Intell 34(7), 1281–1298 (2011)
Qiao, X., Ren, P., Dustdar, S., Chen, J.: A new era for web ar with mobile edge computing. IEEE Internet Comput. 22(4), 46–55 (2018)
Xie, S., Tu, Z.: Holistically-nested edge detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1395–1403 (2015)
Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 122–138. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_8
Chen, S., Tan, X., Wang, B., Lu, H., Hu, X., Fu, Y.: Reverse attention-based residual network for salient object detection. IEEE Trans. Image Process. 29, 3763–3776 (2020)
Lu, P., Zhang, H., Peng, X., Jin, X.: An end-to-end neural network for image cropping by learning composition from aesthetic photos. arXiv preprint arXiv:1907.01432 (2019)
Jian, S., Kaiming, H., Shaoqing, R., Xiangyu, Z.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision & Pattern Recognition, pp. 770–778 (2016)
Prakash, C.S., Panzade, P.P., Om, H., Maheshkar, S.: Detection of copy-move forgery using akaze and sift keypoint extraction. Multimedia Tools Appli. 78, 23535–23558 (2019)
Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.Y.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2010)
Li, G., Yu, Y.: Visual saliency detection based on multiscale deep cnn features. IEEE Trans. Image Process. 25(11), 5012–5024 (2016)
Shi, J., Yan, Q., Xu, L., Jia, J.: Hierarchical image saliency detection on extended cssd. IEEE Trans. Pattern Anal. Mach. Intell. 38(4), 717–729 (2015)
Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S.: Salient object detection: a discriminative regional feature integration approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2083–2090 (2013)
Li, G., Yu, Y.: Deep contrast learning for salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 478–487 (2016)
Hou, Q., Cheng, M.M., Hu, X., Borji, A., Tu, Z., Torr, P.H.: Deeply supervised salient object detection with short connections. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3203–3212 (2017)
Wang, W., Shen, J., Dong, X., Borji, A.: Salient object detection driven by fixation prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1711–1720 (2018)
Liu, N., Han, J., Yang, M.H.: Picanet: learning pixel-wise contextual attention for saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3089–3098 (2018)
Wei, X.S., Cui, Q., Yang, L., Wang, P., Liu, L.: Rpc: a large-scale retail product checkout dataset. arXiv preprint arXiv:1901.07249 (2019)
Acknowledgment
This research was funded in part by the National Natural Science Foundation of China under Grant 62202065, in part by the Project funded by China Postdoctoral Science Foundation 2022TQ0047 and 2022M710465, and in part by Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center.
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Huang, Y. et al. (2024). Cloud-Edge-Device Collaborative Image Retrieval and Recognition for Mobile Web. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 562. Springer, Cham. https://doi.org/10.1007/978-3-031-54528-3_26
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