A Framework of Extracting Multi-scale Features Using Multiple Convolutional Neural Networks Introduction Introduction How do machines describe images How do machines describe images

A Framework of Extracting Multi-scale Features Using Multiple Convolutional Neural Networks Introduction Introduction How do machines describe images How do machines describe images www.phwiki.com

A Framework of Extracting Multi-scale Features Using Multiple Convolutional Neural Networks Introduction Introduction How do machines describe images How do machines describe images

D’Anna, John, Mesa City Editor has reference to this Academic Journal, PHwiki organized this Journal A Framework of Extracting Multi-scale Features Using Multiple Convolutional Neural NetworksKuan-Chuan PengTsuhan Chen1IntroductionBreakthrough progress in object classification.2O. Russakovsky et al. ImageNet large scale visual recognition challenge. arXiv:1409.0575, 2014.N. Murray et al. AVA: A Large-Scale Database as long as Aesthetic Visual Analysis. CVPR12.IntroductionHumans are interested in more than objects.For example, aesthetic quality.3N. Murray et al. AVA: A Large-Scale Database as long as Aesthetic Visual Analysis. CVPR12.

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How do machines describe imagesExamples by state-of-art algorithm:A. Karpathy in addition to F.-F. Li. Deep visual-semantic alignments as long as generating image descriptions. CVPR15.http://cs.stan as long as d.edu/people/karpathy/deepimagesent/4How do machines describe imagesExamples by state-of-art algorithm:A. Karpathy in addition to F.-F. Li. Deep visual-semantic alignments as long as generating image descriptions. CVPR15.http://cs.stan as long as d.edu/people/karpathy/deepimagesent/5How do machines describe imagesExamples by state-of-art algorithm:A. Karpathy in addition to F.-F. Li. Deep visual-semantic alignments as long as generating image descriptions. CVPR15.http://cs.stan as long as d.edu/people/karpathy/deepimagesent/6

How do machines describe imagesExamples by state-of-art algorithm:A. Karpathy in addition to F.-F. Li. Deep visual-semantic alignments as long as generating image descriptions. CVPR15.http://cs.stan as long as d.edu/people/karpathy/deepimagesent/7How do experts describe imagesExamples by the Pulitzer Prize winners:http://www.pulitzer.org/archives/8417http://www.pulitzer.org/archives/64518How do experts describe imagesImages convey more than objects.http://www.pulitzer.org/archives/8417http://www.pulitzer.org/archives/64519

Beyond ObjectsAbstract attributes matter.Attributes relating to or involving general ideas or qualities rather than specific people, objects, or actions. [Merriam-Webster dictionary]Bridge the gap between machines in addition to humans:Teach machines to solve abstract tasks (tasks involving abstract attributes).http://www.merriam-webster.com/dictionary/abstract10GoalA general framework to achieve better per as long as mance in abstract tasks.Multi-scale features by using convolutional neural networks (CNN).11Why CNN12O. Russakovsky et al. ImageNet large scale visual recognition challenge. arXiv:1409.0575, 2014.L. Deng et al. A deep convolutional neural network using heterogeneous pooling as long as trading acoustic invariance with phonetic confusion. ICASSP13.A. Karpathy et al. Large-scale video classification with convolutional neural networks. CVPR14.

Existing Abstract TasksMore in addition to more abstract tasks are proposed.131415

16InspirationIt is tricky to describe abstract attributes as objects.Not easy to “locate” abstract attributes.What if abstract attributes prevail everywhereLabel-inheritable (LI) property.17Label-Inheritable (LI) Property18[1] F. S. Khan et al. Painting-91: a large scale database as long as computational painting categorization. Machine Vision & Applications 14.[2] Z. Xu et al. Architectural style classification using multinomial latent logistic regression. ECCV14.[3] F.-F. Li et al. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. CVPRW04.

Label-Inheritable (LI) Property19[1] F. S. Khan et al. Painting-91: a large scale database as long as computational painting categorization. Machine Vision & Applications 14.[2] Z. Xu et al. Architectural style classification using multinomial latent logistic regression. ECCV14.[3] F.-F. Li et al. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. CVPRW04.Label-Inheritable (LI) Property20[1] F. S. Khan et al. Painting-91: a large scale database as long as computational painting categorization. Machine Vision & Applications 14.[2] Z. Xu et al. Architectural style classification using multinomial latent logistic regression. ECCV14.[3] F.-F. Li et al. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. CVPRW04.Multi-Scale CNNAssume LI property holds as long as each image in addition to the associated label.21A. Krizhevsky et al. ImageNet classification with deep convolutional neural networks. NIPS12.

AlexNetThe number of nodes in output layer is changed to be the number of classes in each task.22A. Krizhevsky et al. ImageNet classification with deep convolutional neural networks. NIPS12.Experimental Results[1] F. S. Khan et al. Painting-91: a large scale database as long as computational painting categorization. Machine Vision & Applications 14.[2] M. D. Zeiler in addition to R. Fergus. Visualizing in addition to underst in addition to ing convolutional networks. ECCV14.[3] Z. Xu et al. Architectural style classification using multinomial latent logistic regression. ECCV14.classification accuracy (%)23Is it because of more training dataWhat if we train one CNN with images in different scales24A. Krizhevsky et al. ImageNet classification with deep convolutional neural networks. NIPS12.

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Additional Results[1] F. S. Khan et al. Painting-91: a large scale database as long as computational painting categorization. Machine Vision & Applications 14.[2] M. D. Zeiler in addition to R. Fergus. Visualizing in addition to underst in addition to ing convolutional networks. ECCV14.[3] Z. Xu et al. Architectural style classification using multinomial latent logistic regression. ECCV14.classification accuracy (%)25ConclusionWe proposed Multi-Scale Convolutional Neural Networks (MSCNN) based on Label-Inheritable (LI) property.Multi-scale features.MSCNN can outper as long as m the state-of-art per as long as mance on datasets where LI property holds or even partially holds.26Towards Solving Abstract TasksMore CNN features to achieve better per as long as mance in abstract tasks.Multi-scale features (ICME15).Multi-depth features (ICIP15).Multi-task features (submitted to ICCV15).27K.-C. Peng in addition to T. Chen. A Framework of extracting multi-scale features using multiple convolutional neural networks. ICME15.K.-C. Peng in addition to T. Chen. Cross-layer features in convolutional neural networks as long as generic classification tasks. ICIP15.K.-C. Peng in addition to T. Chen. Toward correlating in addition to solving abstract tasks using convolutional neural networks. Submitted to ICCV15.

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