Abstract
Content based image retrieval plays an important role in the management of a large image database. However, the results of state-of-the-art image retrieval approaches are not so satisfactory for the well-known gap between visual features and semantic concepts. Therefore, a novel transductive learning scheme named random walk with restart based method (RWRM) is proposed, consisting of three major components: pre-filtering processing, relevance score calculation, and candidate ranking refinement. Firstly, to deal with the problem of large computation cost involved in a large image database, a pre-filtering processing is utilized to filter out the most irrelevant images while keeping the most relevant images according to the results of a manifold ranking algorithm. Secondly, the relevance between a query image and the remaining images are obtained with respect to the probability density estimation. Finally, a transductive learning model, namely a random walk with restart model, is utilized to refine the ranking taking into account both the pairwise information of unlabeled images and the relevance scores between query image and unlabeled images. Experiments conducted on a typical Corel dataset demonstrate the effectiveness of the proposed scheme.
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References
Akakin, H., & Gurcan, M. (2012). Content-based microscopic image retrieval system for multi-image queries. IEEE Transactions on Information Technology in Biomedicine, 16(4), 758–769.
Beecks, C., Assent, I., Seidl, T. (2011). Content-based multimedia retrieval in the presence of unknown user preferences. In Proceeding of IEEE conference on multimedia modeling (pp. 140–150).
Bower, R., & Balogh, M. (2004). The difference between clusters and groups: a journey from cluster cores to their outskirts and beyond. In Carnegie observatories astrophysics series (pp. 1–20).
Cui, J., & Zhang, C. (2007). Combining stroke-based and selection-based relevance feedback for content-based image retrieval. In Proceedings of the ACM conference on multimedia (pp. 329–332).
Gunnarsson, O., & Jones, R. (1980). Density functional calculations for atoms, molecules and clusters. Physica Scripta, 21(3–4), 394–401.
He, J., Li, M., Zhang, H., Tong, H., Zhang, C. (2004). Manifold-ranking based image retrieval. In Proceedings of the ACM conference on multimedia (pp. 9–16).
He, J., Li, M., Zhang, H., Tong, H., Zhang, C. (2006). Generalized manifold-ranking based image retrieval. IEEE Transactions on Image Processing, 15(10), 3170–3177.
Jeon, J., Lavrenko, V., Manmatha, R. (2003). Automatic image annotation and retrieval using cross-media relevance models. In Proc. Proceedings of the ACM conference on multimedia information retrieval (pp. 119–126).
Jing, F., Li, M., Zhang, H., Zhang, B. (2002). An effective region-based image retrieval framework. In Proceedings of the ACM conference on multimedia (pp. 456–465).
Karypis, G., & Kumar, V. (1999). Parallel multilevel k-way partitioning for irregular graphs. SIAM Review, 41(2), 278–300.
Kokare, M., Chatterji, B., Biswas, P. (2003). Comparison of similarity metrics for texture image retrieval. In Proceedings of the IEEE conference on convergent technologies for Asia-Pacific region (pp. 571–575).
Li, B., Chang, E., Wu, C. (2002). DPF-a perceptual distance function for image retrieval. In Proceedings of the IEEE conference on image processing (pp. 597–600).
Natsev, Naphade, M., Tesic, J. (2005). Learning the semantics of multimedia queries and concepts from a small number of examples. In Proceedings of the ACM conference on multimedia (pp. 598–607).
Pass, G. (1997). Comparing images using color coherence vectors. In Proceedings of the ACM conference on multimedia (pp. 65–73).
Rubner, Y., Tomasi, C., Guibas, L. (1998). A metric for distributions with applications to image databases. In Proceedings of the IEEE conference on computer vision (pp. 59–66).
Stricker, M., & Orengo, M. (1995). Similarity of color images. In Proceedings of the IEEE conference on storage and retrieval for image and video databases (pp. 381–392).
Su, J., Huang, W., Yu, P., Tseng, V. (2011). Efficient relevance feedback for content-based image retrieval by mining user navigation patterns. IEEE Transactions on Knowledge and Data Engineering, 23(3), 360–372.
Wang, B., Pan, F., Hu, K., Paul, J. (2012). Manifold-ranking based retrieval using k-regular nearest neighbor graph. Pattern Recognition, 45(4), 1569–1577.
Xu, B., Bu, J., Chen, C. (2011). Efficient manifold ranking for image retrieval. In Proceedings of the ACM SIGIR conference on research and development in information retrieval (pp. 525–534).
Yuan, X., Hua, X., Wang, M., Wu, X. (2006). Manifold-ranking based video concept detection on large database and feature pool. In Proceedings of the ACM conference on multimedia (pp. 623–628).
Zhang, R., & Zhang, Z. (2004). Hidden semantic concept discovery in region based image retrieval. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 996–1001).
Zhang, L., Wang, L., Lin, W. (2012). Generalized biased discriminant analysis for content-based image retrieval. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 42(1), 282–290.
Acknowledgements
This work is supported by Research Fund for the Doctoral Program of Higher Education of China under No. 20103223120003, Natural Science Fund of Jiangsu Province under No. BK2011758 and BK2012832, National Natural Science Foundation of China under No 61104216, 61203270 and 31200496.
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Zhu, S., Zou, L. & Fang, B. Content based image retrieval via a transductive model. J Intell Inf Syst 42, 95–109 (2014). https://doi.org/10.1007/s10844-013-0257-4
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DOI: https://doi.org/10.1007/s10844-013-0257-4