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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 11/2019

07.06.2018 | Original Article

Sitcom-star-based clothing retrieval for video advertising: a deep learning framework

verfasst von: Haijun Zhang, Yuzhu Ji, Wang Huang, Linlin Liu

Erschienen in: Neural Computing and Applications | Ausgabe 11/2019

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Abstract

This paper presents a novel learning-based framework for video content-based advertising, DeepLink, which aims at linking Sitcom-stars and online shops with clothing retrieval by using state-of-the-art deep convolutional neural networks (CNNs). Specifically, several deep CNN models are adopted for composing multiple sub-modules in DeepLink, including human-body detection, human pose selection, face verification, clothing detection and retrieval from advertisements (ads) pool that is constructed by clothing images crawled from real-world online shops. For clothing detection and retrieval from ad-images, we firstly transfer the state-of-the-art deep CNN models to our data domain, and then train corresponding models based on our constructed large-scale clothes datasets. Extensive experimental results demonstrate the feasibility and efficacy of our proposed clothing-based video advertising system.
Vorheriger Artikel Sparse regularized discriminative canonical correlation analysis for multi-view semi-supervised learning
Nächster Artikel Topological structure regularized nonnegative matrix factorization for image clustering

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Literatur
1.
Andriluka M, Pishchulin L, Gehler P, Schiele B (2014) 2d human pose estimation: new benchmark and state of the art analysis. In: 2014 IEEE conference on computer vision and pattern recognition. IEEE, pp 3686–3693
2.
Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2016) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. arXiv preprint arXiv:​1606.​00915
3.
Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009. IEEE, pp 248–255
4.
Erhan D, Szegedy C, Toshev A, Anguelov D (2014) Scalable object detection using deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2147–2154
5.
Erin Liong V, Lu J, Wang G, Moulin P, Zhou J (2015) Deep hashing for compact binary codes learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2475–2483
6.
Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vision 88(2):303–338CrossRef
7.
Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645CrossRef
8.
Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448
9.
Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587
10.
Hadi Kiapour M, Han X, Lazebnik S, Berg AC, Berg TL (2015) Where to buy it: matching street clothing photos in online shops. In: Proceedings of the IEEE international conference on computer vision, pp 3343–3351
11.
He K, Zhang X, Ren S, Sun J (2014) Spatial pyramid pooling in deep convolutional networks for visual recognition. In: European conference on computer vision. Springer, pp 346–361
12.
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
13.
Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507MathSciNetCrossRef
14.
Hou S, Zhou S, Chen L, Feng Y, Awudu K (2016) Multi-label learning with label relevance in advertising video. Neurocomputing 171:932–948CrossRef
15.
Huang GB, Ramesh M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst
16.
Huang J, Feris RS, Chen Q, Yan S (2015) Cross-domain image retrieval with a dual attribute-aware ranking network. In: Proceedings of the IEEE international conference on computer vision, pp 1062–1070
17.
Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:​1502.​03167
18.
Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on multimedia. ACM, pp 675–678
19.
Karpathy A, Fei-Fei L (2015) Deep visual-semantic alignments for generating image descriptions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3128–3137
20.
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
21.
Larsson G, Maire M, Shakhnarovich G (2016) Fractalnet: ultra-deep neural networks without residuals. arXiv preprint arXiv:​1605.​07648
22.
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRef
23.
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRef
24.
Li Y, Wan KW, Yan X, Xu C (2005) Real time advertisement insertion in baseball video based on advertisement effect. In: Proceedings of the 13th annual ACM international conference on Multimedia. ACM, pp 343–346
25.
Lin K, Yang HF, Hsiao JH, Chen CS (2015) Deep learning of binary hash codes for fast image retrieval. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 27–35
26.
27.
28.
29.
Liu Z, Luo P, Qiu S, Wang X, Tang X (2016) Deepfashion: powering robust clothes recognition and retrieval with rich annotations. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1096–1104
30.
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440
31.
López-Nores M, Blanco-Fernández Y, Pazos-Arias JJ (2013) Cloud-based personalization of new advertising and e-commerce models for video consumption. Comput J 56(5):573–592CrossRef
32.
Mei T, Hua XS, Li S (2009) Videosense: a contextual in-video advertising system. IEEE Trans Circuits Syst Video Technol 19(12):1866–1879CrossRef
33.
Murala S, Maheshwari RP, Balasubramanian R (2012) Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans Image Process 21(5):2874–2886MathSciNetCrossRef
34.
Noh H, Hong S, Han B (2015) Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE international conference on computer vision, pp 1520–1528
35.
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRef
36.
Redmon J, Divvala S, Girshick R, Farhadi A (2015) You only look once: unified, real-time object detection. arXiv preprint arXiv:​1506.​02640
37.
Redondo RPD, Vilas AF, Arias JJP, Cabrer MR, Solla AG, Duque JG (2012) Bringing content awareness to web-based idtv advertising. IEEE Trans Syst Man Cybern C (Appl Rev) 42(3):324–333CrossRef
38.
Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99
39.
Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492–1496CrossRef
40.
Schroff F, Kalenichenko D, Philbin J (2015) Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815–823
41.
Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y (2013) Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:​1312.​6229
42.
Shotton J, Sharp T, Kipman A, Fitzgibbon A, Finocchio M, Blake A, Cook M, Moore R (2013) Real-time human pose recognition in parts from single depth images. Commun ACM 56(1):116–124CrossRef
43.
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:​1409.​1556
44.
Sun Y, Chen Y, Wang X, Tang X (2014) Deep learning face representation by joint identification-verification. In: Advances in neural information processing systems, pp 1988–1996
45.
46.
Sun Y, Wang X, Tang X (2013) Deep convolutional network cascade for facial point detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3476–3483
47.
Sun Y, Wang X, Tang X (2014) Deep learning face representation from predicting 10,000 classes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1891–1898
48.
Sun Y, Wang X, Tang X (2015) Deeply learned face representations are sparse, selective, and robust. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2892–2900
49.
Szegedy C, Ioffe S, Vanhoucke V (2016) Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv preprint arXiv:​1602.​07261
50.
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9
51.
52.
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2015) Rethinking the inception architecture for computer vision. arXiv preprint arXiv:​1512.​00567
53.
Tan XY, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650MathSciNetCrossRef
54.
Toshev A, Szegedy C (2014) Deeppose: human pose estimation via deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1653–1660
55.
Uijlings JR, van de Sande KE, Gevers T, Smeulders AW (2013) Selective search for object recognition. Int J Comput Vision 104(2):154–171CrossRef
56.
Wang J, Wang B, Duan LY, Tian Q, Lu H (2014) Interactive ads recommendation with contextual search on product topic space. Multimed Tools Appl 70(2):799–820CrossRef
57.
Wolf L, Hassner T, Maoz I (2011) Face recognition in unconstrained videos with matched background similarity. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 529–534
58.
Xia R, Pan Y, Lai H, Liu C, Yan S (2014) Supervised hashing for image retrieval via image representation learning. In: AAAI, vol 1, p 2
59.
Xie S, Tu Z (2015) Holistically-nested edge detection. In: Proceedings of the IEEE international conference on computer vision, pp 1395–1403
60.
Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhutdinov R, Zemel RS, Bengio Y (2015) Show, attend and tell: neural image caption generation with visual attention. 2(3):5. arXiv preprint arXiv:​1502.​03044 (2015)
61.
Yadati K, Katti H, Kankanhalli M (2014) Cavva: computational affective video-in-video advertising. IEEE Trans Multimed 16(1):15–23CrossRef
62.
63.
Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision. Springer, pp 818–833
64.
Zhang H, Cao X, Ho JK, Chow TW (2017) Object-level video advertising: an optimization framework. IEEE Trans Industr Inf 13(2):520–531CrossRef
Metadaten
Titel
Sitcom-star-based clothing retrieval for video advertising: a deep learning framework
verfasst von
Haijun Zhang
Yuzhu Ji
Wang Huang
Linlin Liu
Publikationsdatum
07.06.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 11/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3579-x

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