Jointly Optimizing 3D Model Fitting and Fine-Grained Classification
Conference paper
Abstract
3D object modeling and fine-grained classification are often treated as separate tasks. We propose to optimize 3D model fitting and fine-grained classification jointly. Detailed 3D object representations encode more information (e.g., precise part locations and viewpoint) than traditional 2D-based approaches, and can therefore improve fine-grained classification performance. Meanwhile, the predicted class label can also improve 3D model fitting accuracy, e.g., by providing more detailed class-specific shape models. We evaluate our method on a new fine-grained 3D car dataset (FG3DCar), demonstrating our method outperforms several state-of-the-art approaches. Furthermore, we also conduct a series of analyses to explore the dependence between fine-grained classification performance and 3D models.
Keywords
Active Shape Model Landmark Location Fisher Vector Pickup Truck Deformable Part Model
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Download
to read the full conference paper text
References
- 1.Andriluka, M., Roth, S., Schiele, B.: People-tracking-by-detection and people-detection-by-tracking. In: CVPR (2008)Google Scholar
- 2.Berg, T., Belhumeur, P.N.: Poof: Part-based one-vs-one features for fine-grained categorization, face verification, and attribute estimation. In: CVPR (2013)Google Scholar
- 3.Cootes, J.G.T.F., Taylor, C.J., Cooper, D.H.: Active shape models—their training and application. In: CVIU (1995)Google Scholar
- 4.Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)Google Scholar
- 5.Deng, J., Krause, J., Fei-Fei, L.: Fine-grained crowdsourcing for fine-grained recognition. In: CVPR (2013)Google Scholar
- 6.Duan, K., Parikh, D., Crandall, D., Grauman, K.: Discovering localized attributes for fine-grained recognition. In: CVPR (2012)Google Scholar
- 7.Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research 9, 1871–1874 (2008)zbMATHGoogle Scholar
- 8.Farrell, R., Oza, O., Zhang, N., Morariu, V.I., Darrell, T., Davis, L.S.: Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance. In: ICCV (2011)Google Scholar
- 9.Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan., D.: Object detection with discriminatively trained part based models. TPAMI (2010)Google Scholar
- 10.Gavves, E., Fernando, B., Snoek, C.G.M., Smeulders, A.W.M., Tuytelaars, T.: Fine-grained categorization by alignments. In: ICCV (2013)Google Scholar
- 11.Guo, Y., Rao, C., Samarasekera, S., Kim, J., Kumar, R., Sawhney, H.: Matching vehicles under large pose transformations using approximate 3d models and piecewise mrf model. In: CVPR (2009)Google Scholar
- 12.Hejrati, M., Ramanan, D.: Analyzing 3d objects in cluttered images. In: NIPS (2012)Google Scholar
- 13.Krause, J., Deng, J., Stark, M., Fei-Fei, L.: Collecting a large-scale dataset of fine-grained cars. In: CVPR-FGCV2 (2013)Google Scholar
- 14.Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3d object representations for fine-grained categorization. In: International IEEE Workshop on 3D Representation and Recognition (2013)Google Scholar
- 15.Leibe, B., Leonardis, A., Schiele, B.: Robust object detection with interleaved categorization and segmentation. IJCV (2007)Google Scholar
- 16.Leotta, M.J., Mundy, J.L.: Vehicle surveillance with a generic, adaptive, 3d vehicle model. TPAMI (2011)Google Scholar
- 17.Li, Y., Gu, L., Kanade, T.: Robustly aligning a shape model and its application to car alignment of unknown pose. TPAMI (2011)Google Scholar
- 18.Liu, J., Kanazawa, A., Jacobs, D., Belhumeur, P.: Dog breed classification using part localization. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 172–185. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 19.Özuysal, M., Lepetit, V., Fua, P.: Pose estimation for category specific multiview object localization. In: CVPR (2009)Google Scholar
- 20.Pepik, B., Gehler, P., Stark, M., Schiele, B.: 3d2pm - 3d deformable part models. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 356–370. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 21.Pepik, B., Stark, M., Gehler, P., Schiele, B.: Teaching 3d geometry to deformable part models. In: CVPR (2012)Google Scholar
- 22.Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 23.Stark, M., Krause, J., Pepik, B., Meger, D., Little, J.J., Schiele, B., Koller, D.: Fine-grained categorization for 3d scene understanding. In: BMVC (2012)Google Scholar
- 24.Tsin, Y., Genc, Y., Ramesh, V.: Explicit 3d modeling for vehicle monitoring in non-overlapping cameras. In: AVSS (2009)Google Scholar
- 25.Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms (2008), http://www.vlfeat.org/
- 26.Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: CVPR (2010)Google Scholar
- 27.Yao, B., Khosla, A., Fei-Fei, L.: Combining randomization and discrimination for fine-grained image categorization. In: CVPR (2011)Google Scholar
- 28.Chai, Y., Lempitsky, V., Zisserman, A.: Symbiotic segmentation and part localization for fine-grained categorization. In: ICCV (2013)Google Scholar
- 29.Zhang, N., Farrell, R., Darrell, T.: Pose pooling kernels for sub-category recognition. In: CVPR (2012)Google Scholar
- 30.Zia, M.Z., Stark, M., Schiele, B., Schindler, K.: Detailed 3d representations for object recognition and modeling. PAMI (2013)Google Scholar
Copyright information
© Springer International Publishing Switzerland 2014