Abstract
Vocabulary tree-based method is one of the most popular methods for content-based image retrieval due to its efficiency and effectiveness. However, for existing vocabulary tree methods, the retrieval precision in large scale image database has never been acceptable especially for image datasets with high variations. In this paper, we propose a novel tree fusion framework: Feature Forest, utilizing and fusing different kind of local visual descriptors to achieve a better retrieval performance. In the offline-learning stage, our framework first establishes different feature vocabulary trees based on different features and uses the average covariance to build vocabulary tree adaptively. In the online-query stage, we use the ratio of the resulting score to the standard score to fuse retrieval results of each vocabulary tree adaptively. The evaluations show the effectiveness of our approach compared with single vocabulary-tree based methods on different databases.
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References
Leibe, B., Schiele, B.: Analyzing contour and appearance based methods for object categorization. In: IEEE Computer Vision and Pattern Recognition (CVPR) (2003)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: IEEE Computer Vision and Pattern Recognition, CVPR (2006)
Frauendorfer, F., Wu, C., Frahm, J.-M., Pollefeys, M.: Visual Word based Location Recognition in 3D models using Distance Augmented Weighting. In: 3D Data Processing Visualization and Transmission(3DPVT) (2008)
Siggelkow, S.: Feature histograms for content-based image retrieval, Ph.D. dissertation Freiburg, Germany: Univ. Freiburg, Dept. Computer Sci. (2002)
Nister, D., Stewenius, H.: Scalable Recognition with a Vocabulary Tree. In: IEEE Computer Vision and Pattern Recognition (CVPR), Washington, DC, USA, pp. 2161–2168 (2006)
Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: International Conference on Computer Vision, ICCV (2003)
Grauman, K., Darrell, T.: Approximate correspondences in high dimensions. In: Advances in Neural Information Processing Systems (NIPS) (2006)
Adaptive Vocabulary Forests for Dynamic Indexing and Category Learning. In: International Conference on Computer Vision, ICCV (2007)
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A.: A Comparison of Affine Region Detectors. International Journal of Computer Vision (IJCV) (2005)
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: IEEE Computer Vision and Pattern Recognition (CVPR) (2005)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Dietterich, T.G., Lathrop, R.H., Lozano-Perez, T.: Solving the Multiple-Instance Problem with Axis-Parallel Rectangles. Artificial Intelligence Journal 89 (1997)
Maron, O., Ratan, A.: A framework for multiple-instance learning. In: Neural Information Processing Systems (NIPS), p. 10
Andoni, A., Indyk, P.: Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions. Communications of the ACM 51, 117–122 (2008)
Andoni, A., Datar, M., Immorlica, N.: Locality-Sensitive Hashing Scheme Based on p-Stable Distributions. MIT Press, Cambridge (2006)
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Song, J., Ma, Y., Hu, F., Zhao, Y., Lao, S. (2010). Scalable Image Retrieval Based on Feature Forest. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12297-2_49
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DOI: https://doi.org/10.1007/978-3-642-12297-2_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12296-5
Online ISBN: 978-3-642-12297-2
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