Abstract
This paper gives an overview of recent approaches towards image representation and image similarity computation for content-based image retrieval and automatic image annotation (category tagging). Additionaly, a new similarity function between an image and an object class is proposed. This similarity function combines various aspects of object class appearance through use of representative images of the class. Similarity to a representative image is determined by weighting local image similarities, where weights are learned from training image pairs, labeled “same” and “different”, using linear SVM. The proposed approach is validated on a challenging dataset where it performed favorably.
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Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)
Berg, A.C., Malik, J.: Geometric blur for template matching. In: CVPR, vol. 1, pp. 607–614 (2001)
Chang, C., Lin, C.: LIBSVM: A library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Duin, R.P.W.: The combining classifier: To train or not to train? In: ICPR (2002)
Fritz, G., Seifert, C., Paletta, L.: A mobile vision system for urban detection with informative local descriptors. In: Computer Vision Systems (2006)
Frome, A., Singer, Y., Malik, J.: Image retrieval and classification using local distance functions. In: NIPS, pp. 417–424. MIT Press, Cambridge, MA (2007)
Gudivada, V.N., Raghavan, V.V.: Content-based image retrieval-systems. Computer 28(9), 18–22 (1995)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. of Fourth Alvey Vision Conf., pp. 147–151 (1988)
Jain, A.K., Vailaya, A.: Image retrieval using color and shape. Pattern Recognition (1996)
Johansson, B., Cipolla, R.: A system for automatic pose-estimation from a single image in a city scene. In: Int. Conf. Signal Proc. Pattern Rec. and Analysis (2002)
Jurie, F., Triggs, B.: Creating efficient codebooks for visual recognition. In: International Conference on Computer Vision (2005)
Kadir, T., Brady, M.: Saliency, scale and image description. International Journal of Computer Vision V45(2), 83–105 (2001)
Ke, Y., Sukthankar, R.: Pca-sift: a more distinctive representation for local image descriptors. In: CVPR 2004, pp. II: 506–513 (2004)
Leung, T., Malik, J.: Recognizing surfaces using three-dimensional textons. In: ICCV, vol. 2, pp. 1010–1017. IEEE, Los Alamitos, CA (1999)
Lindeberg, T.: Feature detection with automatic scale selection. Int. J. Comput. Vision 30(2), 79–116 (1998)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996)
Mikolajczyk, K., Leibe, B., Schiele, B.: Local features for object class recognition. In: ICCV, vol. 2, pp. 1792–1799. IEEE, Los Alamitos, CA (2005)
Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. International Journal of Computer Vision 60(1), 63–86 (2004)
Moosmann, F., Triggs, B., Jurie, F.: Fast discriminative visual codebooks using randomized clustering forests. In: NIPS, pp. 985–992 (2007)
Niculescu-Mizil, A., Caruana, R.: Predicting good probabilities with supervised learning. In: ICML, pp. 625–632. ACM, New York, NY, USA (2005)
Nistér, D., Naroditsky, O., Bergen, J.: Visual odometry for ground vehicle applications. Journal of Field Robotics 23(1), 3–20 (2006)
Nowak, E., Jurie, F.: Learning visual similarity measures for comparing never seen objects. In: CVPR. IEEE, Los Alamitos, CA (2007)
Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification. In: European Conference on Computer Vision. Springer, Heidelberg (2006)
Obdrzalek, S., Matas, J.: Object recognition using local affine frames on distinguished regions. In: BMVA 2002, vol. 1, pp. 113–122 (2002)
Opelt, A., Pinz, A., Fussenegger, M., Auer, P.: Generic object recognition with boosting. IEEE Trans. Pattern Anal. Mach. Intell. 28(3), 416–431 (2006)
Platt, J.: Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. Technical report, Microsoft Research (1999)
Schmid, C., Mohr, R.: Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(5), 530–535 (1997)
Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: ICCV 2003, pp. 1470–1477 (2003)
Steger, C.: An unbiased detector of curvilinear structures. IEEE Trans. Pattern Anal. Mach. Intell. 20(2), 113–125 (1998)
van de Weijer, J., Schmid, C., Verbeek, J.: Learning color names from real-world images. In: CVPR (June 2007)
Winn, J., Criminisi, A., Minka, T.: Object categorization by learned universal visual dictionary. In: Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference, vol. 2, pp. 1800–1807 (2005)
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Krapac, J., Jurie, F. (2008). Learning Distance Functions for Automatic Annotation of Images. In: Boujemaa, N., Detyniecki, M., Nürnberger, A. (eds) Adaptive Multimedia Retrieval: Retrieval, User, and Semantics. AMR 2007. Lecture Notes in Computer Science, vol 4918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79860-6_1
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DOI: https://doi.org/10.1007/978-3-540-79860-6_1
Publisher Name: Springer, Berlin, Heidelberg
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