Abstract
Deep Convolution neural networks (CNN) has achieved great success in the field of image recognition. But in the image retrieval task, the global CNN features ignore local detail description for paying too much attention to semantic information of images. So the MAP of image retrieval remains to be improved. Aiming at this problem, this paper proposes a local CNN feature extraction algorithm based on image understanding, which includes three steps: significant regions extraction, significant regions description and pool coding. This method overcomes the semantic gap problem in traditional local characteristic and improves the retrieval effect of global CNN features. Then, we apply this local CNN feature in the image retrieval task, including the same category retrieval task by feature fusion strategy and the instance retrieval task by re-ranking strategy. The experimental results show that this method has achieved good performance on the Caltech 101 and Caltech 256 classification datasets, and competitive results on the Oxford 5k and Paris 6k instance retrieval datasets.
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
Lowe, D.G.: Object recognition from local scale-invariant features. In: IEEE International Conference on Computer Vision (2001)
Sivic, J.: A text retrieval approach to object matching in videos. In: Proceedings of IEEE International Conference on Computer Vision (2003)
Perronnin, F., Liu, Y., Sánchez, J., Poirier, H.: Large-scale image retrieval with compressed Fisher vectors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Workshops, pp. 3384–3391 (2010)
Jegou, H., Perronnin, F., Douze, M., Sánchez, J., Perez, P.: Aggregating local image descriptors into compact codes. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1704–1716 (2012)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS 2012), pp. 1097–1105 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Workshops, pp. 1–9 (2015)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Proceedings of the European Conference on Computer Vision, pp. 818–833 (2014)
Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Workshops, pp. 1717–1724 (2014)
Hoang, T., Do, T.T., Tan, D.K.L., Cheung, N.M.: Selective deep convolutional features for image retrieval. In: ACM, pp. 1600–1608 (2017)
Xu, J., Shi, C.Z., Qi, C.Z., Wang, C.H., Xiao, B.H.: Unsupervised part-based weighting aggregation of deep convolutional features for image retrieval. In: AAAI2018 (2018)
Razavian, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Workshops, pp. 512–519 (2014)
Babenko, A., Slesarev, A., Chigorin, A., Lempitsky, V.: Neural codes for image retrieval. In: Proceedings of the European Conference on Computer Vision, pp. 584–599 (2014)
Lin, K., Yang, H.F., Hsiao, J.H., Chen, C.S.: Deep learning of binary hash codes for fast image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Workshops, pp. 25–37 (2015)
Wang, J., Song, Y., Leung, T., Rosenberg, C., Wang, J.: Learning fine-grained image similarity with deep ranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1386–1393 (2014)
Chen, Q., Huang, J., Feris, R., Brown, LM., Dong, J.: Deep domain adaptation for describing people based on fine-grained clothing attributes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5315–5324 (2015)
Mairal, J., Koniusz, P., Harchaoui, Z., Schmid, C.: Convolutional kernel networks. In: International Conference on Neural Information Processing Systems. MIT Press, pp. 2627–2635 (2014)
Paulin, M., Douze, M., Harchaoui, Z., Mairal, J., Perronin, F.: Local convolutional features with unsupervised training for image retrieval. In: IEEE International Conference on Computer Vision, pp. 91–99 (2015)
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K.: Visual genome: connecting language and vision using crowdsourced dense image annotations. Int. J. Comput. Vis. 123(1), 32–73 (2017)
Kalantidis, Y., Mellina, C., Osindero, S.: Cross-dimensional weighting for aggregated deep convolutional features. In: Proceedings of the European Conference on Computer Vision. Workshops, pp. 685–701 (2016)
Tolias, G., Sicre, R., Jegou, H.: Particular object retrieval with integral maxpooling of CNN activations. In: Proceedings of the International Conference on Learning Representations, pp. 1–12 (2016)
Jegou, H., Zisserman, A.: Triangulation embedding and democratic aggregation for image search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3310–3317 (2014)
Razavian, A.S., Sullivan, J., Maki, A., Carlsson, S.: Visual instance retrieval with deep convolutional networks (2014). arXiv:1412.6574
Acknowledgements
This work was supported by National Key Research and Development Program (Grant No. 2016YFB0800105), Sichuan Province Scientific and Technological Support Project (Grant Nos. 2016GZ0093, 2018GZ0255), the Fundamental Research Funds for the Central Universities (Grant No. ZYGX2015J009).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Xu, J., Sheng, S., Cai, Y., Bian, Y., Xu, D. (2019). Image Retrieval Research Based on Significant Regions. In: Liu, X., Cheng, D., Jinfeng, L. (eds) Communications and Networking. ChinaCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 262. Springer, Cham. https://doi.org/10.1007/978-3-030-06161-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-06161-6_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-06160-9
Online ISBN: 978-3-030-06161-6
eBook Packages: Computer ScienceComputer Science (R0)