Autoencoder-based image processing framework for object appearance modifications
The presented paper introduces a novel method for enabling appearance modifications for complex image objects. Qualitative visual object properties, quantified using appropriately derived visual attribute descriptors, are subject to alterations. We adopt a basic convolutional autoencoder as a framew...
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| Published in: | Neural computing & applications Vol. 33; no. 4; pp. 1079 - 1090 |
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| Language: | English |
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01.02.2021
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| ISSN: | 0941-0643, 1433-3058 |
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| Abstract | The presented paper introduces a novel method for enabling appearance modifications for complex image objects. Qualitative visual object properties, quantified using appropriately derived visual attribute descriptors, are subject to alterations. We adopt a basic convolutional autoencoder as a framework for the proposed attribute modification algorithm, which is composed of the following three steps. The algorithm begins with the extraction of attribute-related information from autoencoder’s latent representation of an input image, by means of supervised principal component analysis. Next, appearance alteration is performed in the derived feature space (referred to as ‘attribute-space’), based on appropriately identified mappings between quantitative descriptors of image attributes and attribute-space features. Finally, modified attribute vectors are transformed back to latent representation, and output image is reconstructed in the decoding part of an autoencoder. The method has been evaluated using two datasets: images of simple objects—digits from MNIST handwritten-digit dataset and images of complex objects—faces from CelebA dataset. In the former case, two qualitative visual attributes of digit images have been selected for modifications: slant and aspect ratio, whereas in the latter case, aspect ratio of face oval was subject to alterations. Evaluation results prove, both in qualitative and quantitative terms, that the proposed framework offers a promising tool for visual object editing. |
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| AbstractList | The presented paper introduces a novel method for enabling appearance modifications for complex image objects. Qualitative visual object properties, quantified using appropriately derived visual attribute descriptors, are subject to alterations. We adopt a basic convolutional autoencoder as a framework for the proposed attribute modification algorithm, which is composed of the following three steps. The algorithm begins with the extraction of attribute-related information from autoencoder’s latent representation of an input image, by means of supervised principal component analysis. Next, appearance alteration is performed in the derived feature space (referred to as ‘attribute-space’), based on appropriately identified mappings between quantitative descriptors of image attributes and attribute-space features. Finally, modified attribute vectors are transformed back to latent representation, and output image is reconstructed in the decoding part of an autoencoder. The method has been evaluated using two datasets: images of simple objects—digits from MNIST handwritten-digit dataset and images of complex objects—faces from CelebA dataset. In the former case, two qualitative visual attributes of digit images have been selected for modifications: slant and aspect ratio, whereas in the latter case, aspect ratio of face oval was subject to alterations. Evaluation results prove, both in qualitative and quantitative terms, that the proposed framework offers a promising tool for visual object editing. |
| Author | Kapusta, Paweł Kucharski, Jacek Ślot, Krzysztof |
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| Cites_doi | 10.1109/CVPR.2018.00143 10.1109/CVPR.2015.7298932 10.1109/ICCV.2015.419 10.1109/TIP.2019.2916751 10.1007/978-3-319-46493-0_47 10.1007/978-3-642-21735-7_7 10.1109/ICIP.2017.8296650 10.1109/ICCV.2017.629 10.1016/j.patcog.2010.12.015 |
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| Keywords | Convolutional autoencoders Supervised principal component analysis Visual object editing Machine learning |
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| References_xml | – reference: Sotelo J, Mehri S, Kumar K, Santos JF, Kastner K, Courville AC (2017) Char2wav: end-to-end speech synthesis – reference: Masci J, Meier U, Cireşan D, Schmidhuber J (2011) Stacked convolutional auto-encoders for hierarchical feature extraction. In: Artificial neural networks and machine learning—ICANN 2011, Lecture Notes in Computer Science, vol 6791, pp 52–59 – reference: Brock A, Lim T, Ritchie JM, Weston N (2016) Neural photo editing with introspective adversarial networks. arXiv:1609.07093 – reference: HeZZuoWKanMShanSChenXAttgan: facial attribute editing by only changing what you wantIEEE Trans Image Process2019281154645478399929110.1109/TIP.2019.2916751 – reference: Zhang H, Xu T, Li H, Zhang S, Wang X, Huang X, Metaxas D (2017) Stackgan: text to photo-realistic image synthesis with stacked generative adversarial networks. arXiv:1612.03242 – reference: BarshanEGhodsiAAzimifarZZolghadri JahromiMSupervised principal component analysis: visualization, classification and regression on subspaces and submanifoldsPattern Recognit20114471357137110.1016/j.patcog.2010.12.015 – reference: Reed S, Akata Z, Yan X, Logeswaran L, Schiele B, Lee H (2016) Generative adversarial text to image synthesis. arXiv:1605.05396 – reference: Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y (2015) Show, attend and tell: neural image caption generation with visual attention. In: Proceedings of the 32nd international conference on machine learning, volume 37 of proceedings of machine learning research, pp 2048–2057 – reference: Lample G, Zeghidour N, Usunier N, Bordes A, Denoyer L, Ranzato M (2017) Fader networks: manipulating images by sliding attributes. arXiv:1706.00409 – reference: Karpathy A, Fei-Fei L (2015) Deep visual-semantic alignments for generating image descriptions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 3128–3137 – reference: van den Oord Aä, Kalchbrenner N, Kavukcuoglu K (2015) Pixel recurrent neural networks. arXiv:1601.06759 – reference: He Z, Zuo W, Kan M, Shan S, Chen X (2020) Attgan: facial attribute editing by only changing what you want—Tensorflow implementation. https://github.com/LynnHo/AttGAN-Tensorflow – reference: Goodfellow IJ (2017) NIPS 2016 tutorial: Generative adversarial networks. arXiv:1701.00160 – reference: Pieters M, Wiering M (2018) Comparing generative adversarial network techniques for image creation and modification. arXiv:1803.09093 – reference: Sutskever I, Vinyals D, Le QV (2014) Sequence to sequence learning with neural networks. In: Proceedings of the 27th international conference on neural information processing systems, vol 2, NIPS’14, pp 3104–3112 – reference: Xu T, Zhang P, Huang Q, Zhang H, Gan Z, Huang X, He X (2017) AttnGAN: fine-grained text to image generation with attentional generative adversarial networks. arXiv:1711.10485 – reference: Baek K, Bang D, Shim H (2018) Editable generative adversarial networks: generating and editing faces simultaneously. arXiv:1807.07700 – reference: Bodnar C (2018) Text to image synthesis using generative adversarial networks. arXiv:1805.00676 – reference: Oord A, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior A, Kavukcuoglu K (2016) Wavenet: a generative model for raw audio. arXiv:1609.03499 – reference: Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434 – reference: Yan X, Yang J, Sohn K, Lee H (2016) Attribute2Image: conditional image generation from visual attributes. In: European conference on computer vision – reference: Bengio Y, Thibodeau-Laufer E, Yosinski J (2013) Deep generative stochastic networks trainable by backprop. arXiv:1306.1091 – reference: Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv:1312.6114 – reference: Yang S, Luo P, Loy CC, Tang X (2015) From facial parts responses to face detection: a deep learning approach. In: IEEE international conference on computer vision (ICCV) – reference: Gregor K, Danihelka I, Graves A, Rezende D, Wierstra D (2015) Draw: a recurrent neural network for image generation. In: Proceedings of the 32nd international conference on machine learning, volume 37 of proceedings of machine learning research, pp 1462–1471 – reference: Perarnau G, van de Weijer J, Raducanu B, Álvarez JM (2016) Invertible conditional gans for image editing. arXiv:1611.06355 – reference: Mehri S, Kumar K, Gulrajani I, Kumar R, Jain S, Sotelo J, Courville AC, Bengio Y (2016) SampleRNN: an unconditional end-to-end neural audio generation model. arXiv:1612.07837 – reference: Brock A, Donahue J, Simonyan K (2018) Large scale GAN training for high fidelity natural image synthesis. arXiv:1809.11096 – reference: Gorijala M, Dukkipati A (2017) Image generation and editing with variational info generative adversarial networks. arXiv:1701.04568 – reference: KingDEDlib-ml: a machine learning toolkitJ Mach Learn Res20091017551758 – reference: LeCun Y, Cortes C (2010) MNIST handwritten digit database – reference: Antipov G, Baccouche M, Dugelay J (2017) Face aging with conditional generative adversarial networks. arXiv:1702.01983 – reference: He H, Yu PS, Wang C (2018) An introduction to image synthesis with generative adversarial nets. arXiv:1803.04469 – reference: Paulus R, Xiong C, Socher R (2017) A deep reinforced model for abstractive summarization. arXiv:1705.04304 – reference: Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv:1411.1784 – ident: 4976_CR16 – ident: 4976_CR32 doi: 10.1109/CVPR.2018.00143 – ident: 4976_CR14 doi: 10.1109/CVPR.2015.7298932 – ident: 4976_CR10 – ident: 4976_CR34 doi: 10.1109/ICCV.2015.419 – ident: 4976_CR13 doi: 10.1109/TIP.2019.2916751 – ident: 4976_CR6 – ident: 4976_CR18 – ident: 4976_CR31 – ident: 4976_CR33 doi: 10.1007/978-3-319-46493-0_47 – ident: 4976_CR2 – ident: 4976_CR4 – ident: 4976_CR9 – ident: 4976_CR19 doi: 10.1007/978-3-642-21735-7_7 – ident: 4976_CR26 – ident: 4976_CR28 – ident: 4976_CR22 – ident: 4976_CR24 – volume: 28 start-page: 5464 issue: 11 year: 2019 ident: 4976_CR12 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2019.2916751 – ident: 4976_CR20 – volume: 10 start-page: 1755 year: 2009 ident: 4976_CR15 publication-title: J Mach Learn Res – ident: 4976_CR17 – ident: 4976_CR1 doi: 10.1109/ICIP.2017.8296650 – ident: 4976_CR7 – ident: 4976_CR11 – ident: 4976_CR30 – ident: 4976_CR5 – ident: 4976_CR8 – ident: 4976_CR29 – ident: 4976_CR27 – ident: 4976_CR35 doi: 10.1109/ICCV.2017.629 – ident: 4976_CR25 – volume: 44 start-page: 1357 issue: 7 year: 2011 ident: 4976_CR3 publication-title: Pattern Recognit doi: 10.1016/j.patcog.2010.12.015 – ident: 4976_CR23 – ident: 4976_CR21 |
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| Snippet | The presented paper introduces a novel method for enabling appearance modifications for complex image objects. Qualitative visual object properties, quantified... |
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| SubjectTerms | Algorithms Artificial Intelligence Aspect ratio Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Datasets Handwriting Image processing Image Processing and Computer Vision Image reconstruction Original Article Principal components analysis Probability and Statistics in Computer Science Representations |
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| Title | Autoencoder-based image processing framework for object appearance modifications |
| URI | https://link.springer.com/article/10.1007/s00521-020-04976-7 https://www.proquest.com/docview/2489114072 |
| Volume | 33 |
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