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(a) bridge (b) bridge + sea (c) bridge + sea + sky (d) bridge + sea + sky + building Figure 2. The 3 most relevant images retrieved by querying with the word or the group of words below them. The retrieved images tend to be more specific with the increasing number of words used in the queries. 4.2.2 Image Retrieval In this setting, we queried random words such as bridge, sea, sky individually or as a group to retrieve the best corresponding images. The text feature vector y of the random words are created the same way as training tag lists of the images. We then solve miny − V h 2 s.t.h 0 for h. Given h, we plug it into x = W h to obtain a corresponding vector x in the image-feature space. Given x, we search for that column vector xi of the training data matrix X for which x − xi is minimal. The four most similiar images are shown in Table 2 that correspond to the words below. 32 high blue water travel water sky cruise trees clouds holiday bridge waves morning reflections rocks cityscape grass sea daybreak yellow ocean tower woods seascape sea railing raining land waterscape waterscape water sky nightscape beach clouds blue sunrise blue stars outdoors holiday afterdark nature red sky reflection castle night landscape bluesky landscape raining raining moonlight walking disneyland yellow yellow middleages city Table 1. Results of automatic image annotation. The taglist corresponds to the first ranked 10 tags retrieved by querying an unknown image. 5. Conclusion and Future Work The work presented in this paper aims at the analysis of images for which there is additional information available. We introduced a new model for multiview clustering that extends the idea of non-negative matrix factorization (NMF) towards the joint analysis of different types of features. We cast multiview NMF as a convex combination of individual optimization problems and adopt the well known multiplicative fixed point algorithm for NMF to this case. The approach avoids ad hoc combinations of different types of features and thus stays true to the nature of different descriptors. The individual optimization problems in our multiview NMF formulation are coupled via a common coefficient matrix. Due to
this coupling, the resulting basis vectors or cluster centroids allow for inferring one type of descriptor (e.g. image labels) from another type of descriptor (e.g. image features). In preliminary experiments we validated the applicability of the proposed approach in image segmentation, tag prediction, and tag-based image retrieval. Our first results suggest that multiview clustering can provide a framework for image analysis that applies to different levels of abstraction. Image parts could be identified by combining pixel-color and -location information in the principal manner that is provided by the multiview approach. Information as diverse as color histograms and text-by-image vectors were coupled using our framework and we found it to be capable to predict missing information from what data was available. Currently, we are conducting more extensive experiments to provide a more quantitative analysis as well as to compare the proposed approach to other multiview methods such as (kernelized) canonical component analysis. In contrast to related methods from the literature, we expect that highly efficient implementations of multiview NMF will be possible. To this end, we are currently adopting techniques such as convex-hull NMF to our model. We will also further explore how multiview NMF relates to LDA and whether it offers an alternative approach to hierarchical latent topic models. Finally, we envision further applications of the proposed method, for instance in the area of hyperspectral imaging. References [1] D. Blei, A. Ng, and M. Jordan. Latent Dirichlet Allocation. J. of Machine Learning Research, 3(Jan. 2003):993–1022, 2003. 4 [2] M. Borga, T. Landelius, and H. Knutsson. A Unified Approach to PCA, PLS, MLR and CCA. Technical Report LiTH-ISY-R-1992, ISY, Linköping University, 1997. 1 [3] K. Chaudhuri, S. Kakade, K. Liescu, and K. Shridharan. Multi-View Clustering via Canonical Correlation Analysis. In Proc. ICML, 2009. 1 [4] A. Cheriyadat and R. Radke. Non-Negative Matrix Factorization of Partial Track Data for Motion Segmentation. In Proc. IEEE ICCV, 2009. 3 [5] C. Ding, T. Li, and W. Peng. NMF and PLSI: Equivalence and a Hybrid Algorithm. In Proc. ACM SI- GIR, 2006. 4 [6] D. Donoho and V. Stodden. When Does Nonnegative Matrix Factorization Give a Correct Decomposition into Parts? In Proc. NIPS, 2004. 3 [7] K. Fukumizu, F. Bach, and A. Gretton. Statistical Consistency of Kernel Canonical Correlation Analysis. J. of Machine Learning Research, 8(2):361–383, 2007. 1 [8] E. Gaussier and C. Goutte. Relations between PLSA and NMF and Implications. In Proc. ACM SIGIR, 2005. 4 [9] G. Golub and J. van Loan. Matrix Computations. Johns Hopkins University Press, 3rd edition, 1996. 2 [10] D. Guillamet, J. Vitria, and B. Schiele. Introducing a Weighted Non-negative Matrix Factorization for Image Classification. Pattern Recognition Letters, 24(14):2447–2454, 2003. 3 [11] D. Hardoon and J. Shaw-Taylor. Convergence Analysis of Kernel Canonical Correlation Analysis: Theory and Practice. Machine Learning, 74(1):23–38, 2009. 1 [12] H. Hotelling. Relations Between Two Sets of Variates. Biometrika, 28(3–4):321–377, 1936. 1 [13] I. Jolliffe. Principal Component Analysis. Springer, 1986. 2 [14] B. Klingenberg, J. Curry, and A. Dougherty. Nonnegative Matrix Factorization: Ill-posedness and a Geometric Algorithm. Pattern Recognition, 42(5):918–928, 2008. 3 [15] A. Langville, C. Meyer, and R. Albright. Initializations for the Nonnegative Matrix Factorization. In Proc. ACM KDD, 2006. 3 [16] D. Lee and H. Seung. Algorithms for Non-negative Matrix Factorization. In Proc. NIPS, 2000. 2, 3 [17] D. D. Lee and H. S. Seung. Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 401(6755):788–799, 1999. 2, 3 [18] D. MacKay and L. Peto. A Hierarchical Dirichlet Language Model. Natural Language Engineering, 1(3):1–19, 1995. 4 [19] G. Salton and M. J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, New York u.a., 1983. 5 [20] E. Shechtman and M. Irani. Matching local selfsimilarities across images and videos. In IEEE Conference on Computer Vision and Pattern Recognition 2007 (CVPR’07), June 2007. 5 [21] W. Tang, Z. Lu, and I. Dhillon. Clustering with Multiple Graphs. In Proc. IEEE ICDM, 2009. 2 [22] C. Thurau and V. Hlavac. Pose Primitive Based Human Action Recognition in Videos or Still Images. In Proc. IEEE CVPR, 2008. 3 [23] C. Thurau, K. Kersting, and C. Bauckhage. Convex Non-Negative Matrix Factorization in the Wild. In Proc. IEEE ICDM, 2009. 2 [24] C. Tomasi and R. Manduchi. Bilateral Filtering for Gray and Color Images. In Proc. IEEE ICCV, 1998. 5 33
Page 1 and 2: Andreas Wendel Sabine Sternig Marti
Page 3 and 4: Preface The 16 th Computer Vision W
Page 5: Contents Structured Learning and Pr
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Page 12 and 13: correct detection and classificatio
Page 14 and 15: than 45×45 pixels. The false posit
Page 16 and 17: Figure 10. Relative translational d
Page 18 and 19: method is used to decrease the fals
Page 20 and 21: sented in Section 4. The paper is c
Page 22 and 23: Figure 7. Bottom band height ¯ bt
Page 24 and 25: Procedure findBestConfiguration (G)
Page 26 and 27: extremal regions. Image and Vision
Page 28 and 29: extensions of spectral clustering t
Page 30 and 31: Since the coefficient matrix H now
Page 34 and 35: [25] N. Vasiloglou, A. Gray, and D.
Page 36 and 37: Figure 2. Overview of content-based
Page 38 and 39: Geometric Hashing [24]. Our descrip
Page 40 and 41: it shows the following properties.
Page 42 and 43: input feature types such as color a
Page 44 and 45: fast enough for real-time applicati
Page 46 and 47: Figure 3. Edges in our method are c
Page 48 and 49: Figure 8. Post-processing error. If
Page 50 and 51: Figure 14. Incremental superpixels
Page 52 and 53: timate the real camera orientation
Page 54 and 55: Figure 2. Matched features in the E
Page 56 and 57: Image Coordinates Y (px) 0 100 200
Page 58 and 59: Acknowledgment This work was perfor
Page 60 and 61: tion based on constraints derived f
Page 62 and 63: open source computer algebra system
Page 64 and 65: the respective experiment, gj = 0 f
Page 66 and 67: 6. Conclusion In this paper, we pro
Page 68 and 69: dle better in each step. The users
Page 70 and 71: dles grow separately on the branch.
Page 72 and 73: Ash Beech Black pine Fir Ash 1 1 1
Page 74 and 75: [8] Z.-K. Huang, C.-H. Zheng, J.-X.
Page 76 and 77: and is dependent on the incident an
Page 78 and 79: Figure 2: Arc- or circle-like shape
Page 80 and 81: port for a particular circle. Inste
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we cannot entirely evaluate the det
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sufficient for racket classificatio
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its center position: The result of
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Figure 10. Errors of real sequence
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16 th Computer Vision Winter Worksh
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3. New failure prediction methods I
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Sequence name frames First appeared
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Figure 6: Exmaples of randomly gene
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cal descriptors. Recently, Fabbri a
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In our experiments the number of bi
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Figure 6. Camera poses of the stere
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experimental results are obtained o
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tion 1.1 introduces state-of-the-ar
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(a) Convergence of the LP (b) Termi
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N − avg num of tracked patches N
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[17] K. Zimmermann, J. Matas, and T
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