UvA-DARE (Digital Academic Repository) Fine-Grained Categorization by Alignments

The aim of this paper is fine-grained categorization without human interaction. Different from prior work, which relies on detectors for specific object parts, we propose to localize distinctive details by roughly aligning the objects using just the overall shape, since implicit to fine-grained categorization is the existence of a super-class shape shared among all classes. The alignments are then used to trans-fer part annotations from training images to test images (supervised alignment), or to blindly yet consistently seg-ment the object in a number of regions (unsupervised align-ment). We furthermore argue that in the distinction of fine-grained sub-categories, classification-oriented encodings like Fisher vectors are better suited for describing localized information than popular matching oriented features like HOG. We evaluate the method on the CU-2011 Birds and Stanford Dogs fine-grained datasets, outperforming the state-of-the-art.

[1]  Fahad Shahbaz Khan,et al.  Discriminative Color Descriptors , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Peter N. Belhumeur,et al.  POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Trevor Darrell,et al.  Pooling-Invariant Image Feature Learning , 2013, ArXiv.

[4]  Linda G. Shapiro,et al.  Unsupervised Template Learning for Fine-Grained Object Recognition , 2012, NIPS.

[5]  David W. Jacobs,et al.  Dog Breed Classification Using Part Localization , 2012, ECCV.

[6]  Luc Van Gool,et al.  TriCoS: A Tri-level Class-Discriminative Co-segmentation Method for Image Classification , 2012, ECCV.

[7]  Trevor Darrell,et al.  Pose pooling kernels for sub-category recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Gary R. Bradski,et al.  A codebook-free and annotation-free approach for fine-grained image categorization , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Kun Duan,et al.  Discovering localized attributes for fine-grained recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  C. V. Jawahar,et al.  Cats and dogs , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Fei-Fei Li,et al.  Novel Dataset for Fine-Grained Image Categorization : Stanford Dogs , 2012 .

[12]  Larry S. Davis,et al.  Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance , 2011, 2011 International Conference on Computer Vision.

[13]  Pietro Perona,et al.  Multiclass recognition and part localization with humans in the loop , 2011, 2011 International Conference on Computer Vision.

[14]  Pietro Perona,et al.  Strong supervision from weak annotation: Interactive training of deformable part models , 2011, 2011 International Conference on Computer Vision.

[15]  Andrew Zisserman,et al.  BiCoS: A Bi-level co-segmentation method for image classification , 2011, 2011 International Conference on Computer Vision.

[16]  Pietro Perona,et al.  The Caltech-UCSD Birds-200-2011 Dataset , 2011 .

[17]  Fei-Fei Li,et al.  Combining randomization and discrimination for fine-grained image categorization , 2011, CVPR 2011.

[18]  Zeynep Akata,et al.  Fisher Vectors for Fine-Grained Visual Categorization , 2011, CVPR 2011.

[19]  Dieter Fox,et al.  Kernel Descriptors for Visual Recognition , 2010, NIPS.

[20]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[21]  Thomas Mensink,et al.  Improving the Fisher Kernel for Large-Scale Image Classification , 2010, ECCV.

[22]  Pietro Perona,et al.  Visual Recognition with Humans in the Loop , 2010, ECCV.

[23]  Koen E. A. van de Sande,et al.  Evaluating Color Descriptors for Object and Scene Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  J. Matas,et al.  Efficient representation of local geometry for large scale object retrieval , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Andrew Zisserman,et al.  Automated Flower Classification over a Large Number of Classes , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.

[28]  Yoram Singer,et al.  Pegasos: primal estimated sub-gradient solver for SVM , 2007, ICML '07.

[29]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[30]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[31]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[32]  Vladimir Kolmogorov,et al.  "GrabCut": interactive foreground extraction using iterated graph cuts , 2004, ACM Trans. Graph..

[33]  Wayne D. Gray,et al.  Basic objects in natural categories , 1976, Cognitive Psychology.