Abstract
Interest points have traditionally been favoured over dense features for image retrieval tasks, where the goal is to retrieve images similar to a query image from an image corpus. While interest points are invariant to scale and rotation, their coverage of the image is not adequate for sub-image retrieval problems, where the query image occupies a small part of the corpus image. On the other hand, dense features provide excellent coverage but lack invariance, as they are computed at a fixed scale and orientation. Recently, we proposed a novel technique of combining dense features with interest points (Bhattacharya and Gavrilova, Vis Comput 29(6–8):491–499, 2013) to leverage the benefits of both worlds. This allows dense features to be scale invariant but not rotation invariant. In this paper, we build on this framework by incorporating rotation invariance for dense features and introducing several improvements in the voting and match score computation stages. Our method can produce high-quality recognition results that outperform bag of words even with geometric verification and several state-of-art methods that have considered spatial information. We achieve significant improvements in terms of both search speed and accuracy over (Bhattacharya and Gavrilova, Vis Comput 29(6–8):491–499, 2013). Experiments on Oxford Buildings, Holidays and UKbench datasets reveal that our method is not only robust to viewpoint and scale changes that occur in real-world photographs but also to geometric transformations.
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Arandjelović, R., Zisserman, A.: Three things everyone should know to improve object retrieval. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 2911–2918 (2012)
Bhattacharya, P., Gavrilova, M.L.: Spatial consistency of dense features within interest regions for efficient landmark recognition. Vis. Comput. 29(6–8), 491–499 (2013)
Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: ACM International Conference on Image and Video Retrieval, pp. 401–408 (2007)
Cao, Y., Wang, C., Li, Z., Zhang, L., Zhang, L.: Spatial-bag-of-features. In: CVPR, pp. 3352–3359 (2010)
Chatfield, K., Lempitsky, V., Vedaldi, A., Zisserman, A.: The devil is in the details: an evaluation of recent feature encoding methods. In: British Machine Vision Conference, pp. 1–12 (2011)
Chum, O., Mikulík, A., Perdoch, M., Matas, J.: Total recall II: query expansion revisited. In: CVPR, pp. 889–896 (2011)
Jegou, H., Douze, M., Schmid, C.: Hamming embedding and weak geometric consistency for large scale image search. In: ECCV, pp. 304–317 (2008)
Jegou, H., Douze, M., Schmid, C.: On the burstiness of visual elements. In: CVPR, pp. 1169–1176 (2009)
Jegou, H., Douze, M., Schmid, C.: Improving bag-of-features for large scale image search. Int. J. Comput. Vis. 87(3), 316–336 (2010)
Lin, Z., Brandt, J.: A local bag-of-features model for large-scale object retrieval. In: European Conference on Computer Vision, pp. 294–308 (2010)
Lowe, David G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: BMVC, pp. 1–10 (2002)
Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vis. 60(1), 63–86 (2004)
Mikulík, A., Perdoch, M., Chum, O., Matas, J.: Learning vocabularies over a fine quantization. Int. J. Comput. Vis. 103(1), 163–175 (2013)
Nistér, D., Stewénius, H.: Scalable recognition with a vocabulary tree. CVPR 2, 2161–2168 (2006)
Philbin, J., Chum, O., Isard, M., Sivic, J., Andrew Z.: Object retrieval with large vocabularies and fast spatial matching. In: CVPR, pp. 1–8 (2007)
Philbin, J., Chum, O., Isard, M., Sivic, J., Andrew, Z.: Lost in quantization: improving particular object retrieval in large scale image databases. In: CVPR, pp. 1–8 (2008)
Vedaldi, A., Fulkerson, B.: VLFeat: an open and portable library of computer vision algorithms. http://www.vlfeat.org/ (2012)
Wang, X., Yang, M., Cour, T., Zhu, S., Yu, K., Han, T.X.: Contextual weighting for vocabulary tree based image retrieval. In: ICCV, pp. 209–216 (2011)
Wu, Z., Ke, Q., Isard, M., Sun, J.: Bundling features for large scale partial-duplicate web image search. In: CVPR, pp. 25–32 (2009)
Yuan, J., Wu, Y., Yang, M.: Discovery of collocation patterns: from visual words to visual phrases. In: CVPR, pp. 1–8 (2007)
Zhang, Y., Jia, Z., Chen, T.: Image retrieval with geometry-preserving visual phrases. In: CVPR, pp. 809–816 (2011)
Zhao, W.: LIP-VIREO: local interest point extraction toolkit. http://www.cs.cityu.edu.hk/wzhao2/ (2010)
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We are thankful to Natural Sciences and Engineering Research Council of Canada and Alberta Innovates Technology Futures for continued support of this research.
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Bhattacharya, P., Gavrilova, M.L. Rotation invariance for dense features inside regions of interest. Vis Comput 30, 569–578 (2014). https://doi.org/10.1007/s00371-014-0964-z
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DOI: https://doi.org/10.1007/s00371-014-0964-z