High-Dimensional Pattern Recognition via Sparse Representation Allen - PowerPoint PPT Presentation

Introduction Sparse Representation Sparse Optimization Wearable Augmented Reality Conclusion High-Dimensional Pattern Recognition via Sparse Representation Allen Y. Yang University of California, Berkeley and Atheer, Inc. UC Berkeley, June 2013 High-Dimensional Pattern Recognition via Sparse Representation http://www.eecs.berkeley.edu/~yang

Introduction Sparse Representation Sparse Optimization Wearable Augmented Reality Conclusion Shifting Paradigms in High-Dimensional Pattern Recognition Face Recognition Yale B CMU Multi-PIE Facebook Photo Tagging High-Dimensional Pattern Recognition via Sparse Representation http://www.eecs.berkeley.edu/~yang

Introduction Sparse Representation Sparse Optimization Wearable Augmented Reality Conclusion Shifting Paradigms in High-Dimensional Pattern Recognition Face Recognition Yale B CMU Multi-PIE Facebook Photo Tagging Object Recognition ETHZ Cows vs Cars Amazon Flow Caltech 101 High-Dimensional Pattern Recognition via Sparse Representation http://www.eecs.berkeley.edu/~yang

Introduction Sparse Representation Sparse Optimization Wearable Augmented Reality Conclusion Shifting Paradigms in High-Dimensional Pattern Recognition Face Recognition Yale B CMU Multi-PIE Facebook Photo Tagging Object Recognition ETHZ Cows vs Cars Amazon Flow Caltech 101 3D Reconstruction Oxford Corridor Berkeley Downtown Google Earth High-Dimensional Pattern Recognition via Sparse Representation http://www.eecs.berkeley.edu/~yang

Introduction Sparse Representation Sparse Optimization Wearable Augmented Reality Conclusion Accurate recognition of HD models presents unique challenges Big data vs small training sets: New theory is needed. ! "#" $ %& & ' ( $ ' )# %( * + ' ( #, - # - ' + ! + . ' ( / , 0 ! ' . $ 12 2 3453 . 367" 89: 34; 35437<4= 1> 351912 ? 5 <@ <? 4; ( 1A347 %47934 ; <? : 7B<7 B3 A3C ? 3D3: 7B3 : 1C C <4 <75844397 C 3D3C ; E <; > <? 4C = 64? 53: <F<? 9; 72 1; 7' 84163<9 5844395? 3; " G 9 < E ? : 3 4<9F? 9F <: : 43; ; ; 619; 143: A= 7B3 ' H6147IG 2 6147 J <9KL %47934L 7B3 A<9K); ; 39? 14 351912 ? ; 7 <C ; 1 ; <? : B3 A3C ? 3D3: 7B<7 7B3 =39 E <; 89: 34D <C 83: <9: 518C : F1 86 A= M N 14 M O 657" PG: 1 917 43F<4: 7B3 : 1C C <4 <; 89: 34D <C 83: <7 7B? ; 61? 97 <F<? 9; 7 7B3 =39L P B3 ; <? : " %9 7B3 17B34 B<9: L %47934 ; <? : 7B<7 B3 7B18FB7 7B<7 P7B3 =39 ? ; ; 7? C C < C ? 77C 3 A? 7 89: 34D<C 83: LP <9: P518C : F1 86 <917B34 M N 14 M O 657"P G 9 <: : ? 7? 19L %47934L E B1 ; <? : B3 E <; ; 63<K? 9F 634; 19<C C =L ; <? : B3 7B18FB7 7B<7 7B3 : 1C C <4 <F<? 9; 7 2 1; 7 ' 84163<9 5844395? 3; E <; P> <? 4C = 64? 53: "P %47934 ; <? : B? ; <9<C =; ? ; 1> 7B3 D <4? 18; 3H5B<9F3 4<73 D<C 83; E <; A<; 3: 19 ; 85B 351912 ? 5 6<47? 58C <4; <; E <F3 4<73 : ? @ 34397? <7? 19; " %47934 ; <? : 7B343 B<: A339 C ? 77C 3 ? 2 6<57 19 ! "#" 74<: 3 : 3Q5? 7 A= 7B3 : 35C ? 93 1>7B3 : 1C C <4 A35<8; 3 <7 7B3 7? 2 3 1>7B3 R C <S< , 5514: L 7B3 : 1C C <4 E <; 3H7432 3C = 1D34D <C 83: <9: 7B<7 7B3 Q4; 7 M O 657 : 35C ? 93 B<: C ? 77C 3 ? 2 6<57" T 3 ; <? : 7B343 E 343 ? 9: ? 5<7? 19; 91E 7B<7 7B3 74<: 3 : 3Q5? 7 E <; A3F? 99? 9F 71 C 3D3C1@ " * 849? 9F 71 J 4<S? C <9: & 3H? 51L %47934 2 <: 3 ? 7 5C 3<4 7B<7 ? 7 E 18C : A3 <C 2 1; 7 ? 2 61; ; ? AC 3 > 14 7B1; 3 518974? 3; 71 3<49 3918FB > 143? F9 3H5B<9F3 71 6<= 7B3 ; 34D? 53 19 7B3? 4 : 3A7; " T 3 ; <? : 7B3 A3; 7 E <= 71 : 3<CE ? 7B 7B? ; E <; 71 8; 3 7B3 61C ? 5? 3; 187C ? 93: ? 9 * 43<; 84= #35437<4= U<2 3; J <K34); : 3A7 ? 9? 7? <7? D3" 50B webpages average 1M pixels average 1B voxels High-Dimensional Pattern Recognition via Sparse Representation http://www.eecs.berkeley.edu/~yang

Introduction Sparse Representation Sparse Optimization Wearable Augmented Reality Conclusion Accurate recognition of HD models presents unique challenges Big data vs small training sets: New theory is needed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webpages average 1M pixels average 1B voxels From desktop to mobile computing: There’s an app for everything! High-Dimensional Pattern Recognition via Sparse Representation http://www.eecs.berkeley.edu/~yang