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Content-Based Image Retrieval Using Iterative Search

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Abstract

The aim of Content-based Image Retrieval (CBIR) is to find a set of images that best match the query based on visual features. Most existing CBIR systems find similar images in low level features, while Text-based Image Retrieval (TBIR) systems find images with relevant tags regardless of contents in the images. Generally, people are more interested in images with similarity both in contours and high-level concepts. Therefore, we propose a new strategy called Iterative Search to meet this requirement. It mines knowledge from the similar images of original queries, in order to compensate for the missing information in feature extraction process. To evaluate the performance of Iterative Search approach, we apply this method to four different CBIR systems (HOF Zhou et al. in ACM international conference on multimedia, 2012; Zhou and Zhang in Neural information processing—international conference, ICONIP 2011, Shanghai, 2011, HOG Dalal and Triggs in IEEE computer society conference on computer vision pattern recognition, 2005, GIST Oliva and Torralba in Int J Comput Vision 42:145–175, 2001 and CNN Krizhevsky et al. in Adv Neural Inf Process Syst 25:2012, 2012) in our experiments. The results show that Iterative Search improves the performance of original CBIR features by about \(20\%\) on both the Oxford Buildings dataset and the Object Sketches dataset. Meanwhile, it is not restricted to any particular visual features.

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References

  1. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167

    Article  Google Scholar 

  2. Cao Y, Wang C, Li Z, Zhang L, Zhang L (2010) Spatial-bag-of-features. In: IEEE conference on computer vision and pattern recognition, CVPR 2010, San Francisco, Ca, Usa, 13–18 June, pp 3352–3359

  3. Chao X, Huiskes MJ, Gritti T, Ciuhu C (2009) A framework for robust feature selection for real-time fashion style recommendation. In: Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics. ACM, pp 35–42

  4. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision Pattern Recognition, pp 886–893

  5. Eitz M, Hays J, Alexa M (2012) How do humans sketch objects? Acm Trans Gr 31(4):44

    Google Scholar 

  6. Huang J, Feris RS, Chen Q, Yan S (2015) Cross-domain image retrieval with a dual attribute-aware ranking network. In: Proceedings of the IEEE International Conference on Computer Vision. pp 1062–1070

  7. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25(2):2012

    Google Scholar 

  8. Liu Z, Luo P, Qiu S, Wang X, Tang X (2016) Deepfashion: powering robust clothes recognition and retrieval with rich annotations. In: IEEE conference on computer vision and pattern recognition, pp 1096–1104

  9. Lu Y, Hu C, Zhu X, Zhang HJ, Yang Q (2000) A unified framework for semantics and feature based relevance feedback in image retrieval systems. In: Eighth ACM international conference on multimedia, pp 31–37

  10. Mohedano E, Mcguinness K, O’Connor NE, Salvador A, Marques F, Giro-I-Nieto X (2016) Bags of local convolutional features for scalable instance search. In: ACM on international conference on multimedia retrieval, pp 327–331

  11. Nguyen NV, Boucher A, Ogier J, Tabbone S (2010) Clusters-based relevance feedback for CBIR: a combination of query movement and query expansion. In: IEEE RIVF international conference on computing and communication technologies, research, innovation, and vision for the future, pp 1–6

  12. Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vision 42(3):145–175

    Article  MATH  Google Scholar 

  13. Philbin J, Chum O, Isard M, Sivic J, Zisserman A (2007) Object retrieval with large vocabularies and fast spatial matching. In: IEEE computer society conference on computer vision pattern recognition, pp 1–8

  14. Quadrianto N, Schuurmans D, Smola AJ (2010) Distributed flow algorithms for scalable similarity visualization. In: IEEE international conference on data mining workshops, pp 1220–1227

  15. Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380

    Article  Google Scholar 

  16. Sun Z, Wang C, Zhang L, Zhang L (2012) Query-adaptive shape topic mining for hand-drawn sketch recognition. In: ACM international conference on multimedia, pp 519–528

  17. Tolias G, Sicre R, Jégou H (2015) Particular object retrieval with integral max-pooling of CNN activations. arXiv preprint arXiv:1511.05879

  18. Uluwitige DCNW, Chappell T, Geva S, Chandran V (2016) Improving retrieval quality using pseudo relevance feedback in content-based image retrieval. In: The international ACM SIGIR conference, pp 873–876

  19. Wan J, Wang D, Hoi SCH, Wu P, Zhu J, Zhang Y, Li J (2014) Deep learning for content-based image retrieval:a comprehensive study. In: ACM international conference on multimedia, pp 157–166

  20. Yagnik J, Strelow D, Ross DA, Lin RS (2011) The power of comparative reasoning. In: International conference on computer vision, pp 2431–2438

  21. Zhou R, Chen L, Zhang L (2012) Sketch-based image retrieval on a large scale database. In: ACM international conference on multimedia, pp 973–976

  22. Zhou R, Zhang L (2011) Contour-based large scale image retrieval. In: Proceedings of International conference on neural information processing, ICONIP 2011, Shanghai, China, November 13–17, 2011, pp 565–572

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (91420302), the Key Basic Research Program of Shanghai Municipality, China (15JC1400103) and the National Basic Research Program of China (2015CB856004).

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  1. Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Zhengzhong Zhou & Liqing Zhang

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  1. Zhengzhong Zhou
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  2. Liqing Zhang
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Correspondence to Liqing Zhang.

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Zhou, Z., Zhang, L. Content-Based Image Retrieval Using Iterative Search. Neural Process Lett 47, 907–919 (2018). https://doi.org/10.1007/s11063-017-9662-y

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