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Results on Oxford flowers 17 (cont
Results on Oxford flowers 17 (cont
Visual Classification with Multi-Task Joint Sparse Representation
Visual Classification with Multi-Task Joint Sparse Representation
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Visual Classification with Multi-Task Joint Sparse Representation

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1Visual Classification with Multi-Task 14mapping. Aggregation. Algorithm 2.
Joint Sparse Representation. Authors: MTJSRC-RKHS.
Xiaotong Yuan and Shuicheng Yan Presenter: 15Column Generation. MTJSRC-CG: take the
Xiaotong Yuan. Learning & Vision columns of each kernel matrix as feature
Research Group, ECE, National University vectors, Objective: Decision:
of Singapore. 16Experiments. Comparing feature
2Problem to Study. … Task: combine combination algorithms Nearest Subspace +
multiple features for visual Combination Sparse Representation +
classification. Training based methods Combination Representative kernel feature
(MKL and SVM ensemble): classifier combination methods in literature
training + combination Our solution: cast (Nilsback, 2008/2009, Varma, 2007, Gehler,
feature combination to a multi-task joint 2009).
sparse representation problem. Color 17Data Sets. Oxford flowers 17 7 Kernels
Texture Shape. In most cases, multiple (Nilsback, 2009) Oxford flowers 102 4
features. Kernels (Nilsback, 2008) Caltech 101 4
3Motivations. … Advances in sparse Kernels (Varma et al., 2007).
representation (SR) for recognition 18Results on Oxford flowers 17.
(Wright et al., 2009) Robust Training free Accuracies by Feature Combination.
Advances in multi-task sparse learning Accuracies by Single Features. NS. SRC.
Separate but related sparse learning tasks MKL (Nilsback, 2009). CG-Boost (Gehler,
Joint sparsity (Zhang 2006, Liu et al., 2009). LPBoost (Gehler, 2009).
2009). MTJSRC-RKHS. MTJSRC-CG. 83.2 ± 2.1. 85.9 ±
4Related Work. Kernel Feature 2.2. 88.2 ± 2.0. 84.8 ± 2.2. 85.4 ± 2.4.
Combination Multiple Kernel Learning 88.1 ± 2.3. 88.9 ± 2.9. Features. NS. SVM
(Varma & Ray, 2007) Boost Individual (Gehler, 2009). MTJSRC-RKHS (K=1).
SVM classifier (Gehler & Nowozin, MTJSRC-CG (K=1). Color. 61.7 ± 3.3. 60.9 ±
2009) Multi-task joint covariate selection 2.1. 64.0 ± 2.1. 64.0 ± 3.3. Shape. 69.9 ±
(Obozinski et al., 2009) Group Lasso (Yuan 3.2. 70.2 ± 1.3. 72.7 ± 0.3. 71.5 ± 0.8.
& Lin, 2006) Multi-task Lasso (Zhang, Texture. 55.8 ± 1.4. 63.7 ± 2.7. 67.6 ±
2006) Sparse representation (SR) for 2.4. 67.6 ± 2.2. HSV. 61.3 ± 0.7. 62.9 ±
recognition (Wright et al., 2009). 2.3. 64.7 ± 4.1. 65.0 ± 3.9. HOG. 57.4 ±
5Our Method. MTJSRC: a Multi-Task Joint 3.0. 58.5 ± 4.5. 61.9 ± 3.6. 62.6 ± 2.7.
Sparse Representation and Classification SIFTint. 70.7 ± 0.7. 70.6 ± 1.6. 74.0 ±
method Utilizing each feature to form a 2.2. 74.0 ± 2.0. SIFTbdy. 61.9 ± 4.2. 59.4
linear representation task Jointly select ± 3.3. 62.4 ± 3.2. 63.2 ± 33.
representative images from few classes 19Results on Oxford flowers 17 (cont.).
Boost the individual features to improve Sparse representation coefficients and
performance Extensions in Kernel-view. reconstruction errors. Color. Shape.
6Joint Sparse Representation. A set of Texture. HSV. HOG. SIFTint. SIFTbdy.
images. K types of features. Objective: 20Results on Oxford flowers 102.
block-level sparse coefficients. Test Accuracies by Feature Combination.
Image. … … … … … Accuracies by Single Features. NS. SRC.
7Formulation. A supervised K-task MKL (Nilsback, 2008). MTJSRC-RKHS.
linear representation problem. General MTJSRC-CG. 59.2. 70.0. 72.8. 73.8. 74.1.
formulation: multi-task least square Features. NS. SVM (Nilsback, 2008).
regression with mixed-norm regularization: MTJSRC-RKHS (K=1). MTJSRC-CG (K=1). HSV.
Less sparse, convex. Sparser, non-convex. 39.8. 43.0. 43.6. 42.5. HOG. 34.9. 49.6.
8Different Mixed-Norms. Joint 46.7. 48.1. SIFTint. 46.6. 55.1. 54.7.
sparsity-inducing (Obozinski et al., 55.2. SIFTbdy. 34.1. 32.0. 33.0. 31.6.
2009). Joint sparsity-inducing (Zhang, 21Results on Caltech 101. Accuracies by
2006). K Independent SR tasks. K Feature Combination (15 train/15 test).
Independent ridge regression tasks. Accuracies by Single Features. NS. SRC.
9Different Mixed-Norms. Joint MKL (Varma, 2007). LPBoost (Gehler, 2009).
sparsity-inducing (Obozinski et al., MTJSRC-RKHS. MTJSRC-CG. 51.7 ± 0.8. 69.2 ±
2009). Joint sparsity-inducing (Zhang, 0.7. 70.0 ± 1.0. 70.7 ± 0.4. 71.0 ± 0.3.
2006). K Independent SR tasks. K 71.4 ± 0.4. Features. NS. SVM (Varma,
Independent ridge regression tasks. 2007). MTJSRC-RKHS (K=1). MTJSRC-CG (K=1).
10Optimization. An Accelerated Proximal GB. 40.8 ± 0.6. 62.6 ± 1.2. 58.3 ± 0.4.
Gradient Method (Tseng, 2008) Generalized 58.5 ± 0.3. PHOW-gray. 45.4 ± 0.9. 63.9 ±
gradient mapping step Aggregation step. 0.8. 65.0 ± 0.7. 64.5 ± 0.5. PHOW-color.
11Classification. Optimal reconstruction 37.3 ± 0.5. 54.5 ± 0.6. 56.1 ± 0.5. 54.4 ±
coefficients: Decision: Feature 0.7. SSIM. 39.8 ± 0.8. 54.3 ± 0.6. 61.8 ±
confidence: learned via Linear Programming 0.6. 59.7 ± 0.4.
Boosting on validation set. 22Conclusions. Multi-task joint sparse
12Algorithm. Algorithm 1. Multi-Task representation is effective to combine
Joint Sparse Representation complementary visual features. For single
Classification. Generalized gradient feature, the kernel-view extensions of
mapping. Aggregation. MTJSRC perform quite competitive to SVM.
13Kernel-View Extensions. Multitask MTJSRC is free of model training.
joint sparse representation in a RKHS. The 23Thank you!
APG optimization is characterized by inner 24Visual Classification with Multi-Task
product of feature vectors. training Joint Sparse Representation. Xiaotong Yuan
kernel matrix. testing kernel vector. and Shuicheng Yan.
14Algorithm. Generalized gradient
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Visual Classification with Multi-Task Joint Sparse Representation

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