ABSTRACT
Fine-grained grocery product recognition via camera is a challenging task to identify the visually similar products with subtle differences by using single-shot training examples. To address this issue? we present a novel hybrid classification approach that combines feature-based matching and one-shot deep learning with a coarse-to-fine strategy. The candidate regions of product instances are first detected and coarsely labeled by recurring features in product images without any training. Then, attention maps are generated to guide the classifier to focus on fine discriminative details by magnifying the influences of the features in the candidate regions of interest (ROI) and suppressing the interferences of the features outside, improving the accuracy of fine-grained grocery products recognition effectively. Our framework also performs a good adaptability which allows existing classifier to be refined without retraining for new coming product classes. As an additional contribution, we collect a new grocery product database with 102 classes from 2 stores. Extensive experiments demonstrate that our approach outperforms the state-of-the-art methods.
- Alaa E Abdel-Hakim and Aly A Farag. 2006. CSIFT: A SIFT descriptor with color invariant characteristics. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Vol. 2. IEEE, 1978--1983. Google ScholarDigital Library
- Shanshan Ai, Caiyan Jia, and Zhineng Chen. 2017. Large-Scale Product Classification via Spatial Attention Based CNN Learning and Multi-class Regression. In International Conference on Multimedia Modeling. Springer, 176--188.Google ScholarCross Ref
- Inc Amazon.com. {n. d.}. Amazon Go. http://amazon.com/go .Google Scholar
- Anelia Angelova, Shenghuo Zhu, and Yuanqing Lin. 2013. Image segmentation for large-scale subcategory flower recognition. In Applications of Computer Vision (WACV), 2013 IEEE Workshop on. IEEE, 39--45. Google ScholarDigital Library
- Herbert Bay, Tinne Tuytelaars, and Luc Van Gool. 2006. Surf: Speeded up robust features. Computer vision--ECCV 2006 (2006), 404--417. Google ScholarDigital Library
- Ipek Baz, Erdem Yoruk, and Mujdat Cetin. 2016. Context-aware hybrid classification system for fine-grained retail product recognition. In Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 2016 IEEE 12th. IEEE, 1--5.Google Scholar
- Thomas Berg, Jiongxin Liu, Seung Woo Lee, Michelle L Alexander, David W Jacobs, and Peter N Belhumeur. 2014. Birdsnap: Large-scale fine-grained visual categorization of birds. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2011--2018. Google ScholarDigital Library
- Liangliang Cao, Jenhao Hsiao, Paloma de Juan, Yuncheng Li, and Bart Thomee. 2016. Incremental Learning for Fine-Grained Image Recognition. In Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval. ACM, 363--366. Google ScholarDigital Library
- Franccois Chollet et almbox. 2015. Keras. https://github.com/fchollet/keras .Google Scholar
- Jia Deng, Jonathan Krause, Michael Stark, and Li Fei-Fei. 2016. Leveraging the wisdom of the crowd for fine-grained recognition. IEEE transactions on pattern analysis and machine intelligence, Vol. 38, 4 (2016), 666--676. Google ScholarDigital Library
- Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. 2010. The pascal visual object classes (voc) challenge. International journal of computer vision, Vol. 88, 2 (2010), 303--338. Google ScholarDigital Library
- Fabio A Faria, Jurandy Almeida, Bruna Alberton, Leonor Patricia C Morellato, Anderson Rocha, and Ricardo da S Torres. 2016. Time series-based classifier fusion for fine-grained plant species recognition. Pattern Recognition Letters, Vol. 81 (2016), 101--109. Google ScholarDigital Library
- Li Fei-Fei, Rob Fergus, and Pietro Perona. 2006. One-shot learning of object categories. IEEE transactions on pattern analysis and machine intelligence, Vol. 28, 4 (2006), 594--611. Google ScholarDigital Library
- Vittorio Ferrari, Tinne Tuytelaars, and Luc Van Gool. 2004. Simultaneous object recognition and segmentation by image exploration. In European Conference on Computer Vision. Springer, 40--54.Google ScholarCross Ref
- Martin A Fischler and Robert C Bolles. 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, Vol. 24, 6 (1981), 381--395. Google ScholarDigital Library
- Annalisa Franco, Davide Maltoni, and Serena Papi. 2017. Grocery product detection and recognition. Expert Systems with Applications, Vol. 81 (2017), 163--176. Google ScholarDigital Library
- ZongYuan Ge, Chris McCool, Conrad Sanderson, Alex Bewley, Zetao Chen, and Peter Corke. 2015. Fine-grained bird species recognition via hierarchical subset learning. In Image Processing (ICIP), 2015 IEEE International Conference on. IEEE, 561--565.Google ScholarCross Ref
- Marian George and Christian Floerkemeier. 2014. Recognizing products: A per-exemplar multi-label image classification approach. In European Conference on Computer Vision. Springer, 440--455.Google ScholarCross Ref
- Thomas W Gruen, Daniel S Corsten, and Sundar Bharadwaj. 2002. Retail out-of-stocks: A worldwide examination of extent, causes and consumer responses .Grocery Manufacturers of America Washington, DC.Google Scholar
- Chen Huang, Zhihai He, Guitao Cao, and Wenming Cao. 2016. Task-driven progressive part localization for fine-grained object recognition. IEEE Transactions on Multimedia, Vol. 18, 12 (2016), 2372--2383. Google ScholarDigital Library
- Daniel P. Huttenlocher, Gregory A. Klanderman, and William J Rucklidge. 1993. Comparing images using the Hausdorff distance. IEEE Transactions on pattern analysis and machine intelligence, Vol. 15, 9 (1993), 850--863. Google ScholarDigital Library
- Philipp Jund, Nichola Abdo, Andreas Eitel, and Wolfram Burgard. 2016. The Freiburg Groceries Dataset. arXiv preprint arXiv:1611.05799 (2016).Google Scholar
- Leonid Karlinsky, Joseph Shtok, Yochay Tzur, and Asaf Tzadok. 2017. Fine-grained recognition of thousands of object categories with single-example training. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4113--4122.Google ScholarCross Ref
- Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Fei-Fei Li. 2011. Novel dataset for fine-grained image categorization: Stanford dogs. In Proc. CVPR Workshop on Fine-Grained Visual Categorization (FGVC), Vol. 2. 1.Google Scholar
- Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov. 2015. Siamese neural networks for one-shot image recognition. In ICML Deep Learning Workshop, Vol. 2.Google Scholar
- Jonathan Krause, Hailin Jin, Jianchao Yang, and Li Fei-Fei. 2015. Fine-grained recognition without part annotations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5546--5555.Google ScholarCross Ref
- Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. 2003. A sparse texture representation using affine-invariant regions. In Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on, Vol. 2. IEEE, II--II.Google ScholarCross Ref
- Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. 2006. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Computer vision and pattern recognition, 2006 IEEE computer society conference on, Vol. 2. IEEE, 2169--2178. Google ScholarDigital Library
- Stefan Leutenegger, Margarita Chli, and Roland Y Siegwart. 2011. BRISK: Binary robust invariant scalable keypoints. In Computer Vision (ICCV), 2011 IEEE International Conference on. IEEE, 2548--2555. Google ScholarDigital Library
- Di Lin, Xiaoyong Shen, Cewu Lu, and Jiaya Jia. 2015. Deep lac: Deep localization, alignment and classification for fine-grained recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1666--1674.Google ScholarCross Ref
- Jingchen Liu and Yanxi Liu. 2013. Grasp recurring patterns from a single view. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2003--2010. Google ScholarDigital Library
- David G Lowe. 2004. Distinctive image features from scale-invariant keypoints. International journal of computer vision, Vol. 60, 2 (2004), 91--110. Google ScholarDigital Library
- Mattias Marder, Sivan Harary, Amnon Ribak, Y Tzur, Sharon Alpert, and Asaf Tzadok. 2015. Using image analytics to monitor retail store shelves. IBM Journal of Research and Development, Vol. 59, 2/3 (2015), 3--1. Google ScholarDigital Library
- Michele Merler, Carolina Galleguillos, and Serge Belongie. 2007. Recognizing groceries in situ using in vitro training data. In Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on. IEEE, 1--8.Google ScholarCross Ref
- Xinyu Ou, Zhen Wei, Hefei Ling, Si Liu, and Xiaochun Cao. 2016. Deep multi-context network for fine-grained visual recognition. In Multimedia & Expo Workshops (ICMEW), 2016 IEEE International Conference on. IEEE, 1--4.Google Scholar
- Ruslan Salakhutdinov, Joshua Tenenbaum, and Antonio Torralba. 2012. One-shot learning with a hierarchical nonparametric bayesian model. In Proceedings of ICML Workshop on Unsupervised and Transfer Learning. 195--206. Google ScholarDigital Library
- M Shapiro. 2009. Executing the best planogram. Professional Candy Buyer, Norwalk, CT, USA (2009).Google Scholar
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).Google Scholar
- Alessio Tonioni and Luigi Di Stefano. 2017. Product recognition in store shelves as a sub-graph isomorphism problem. In International Conference on Image Analysis and Processing. Springer, 682--693.Google ScholarCross Ref
- Koen Van De Sande, Theo Gevers, and Cees Snoek. 2010. Evaluating color descriptors for object and scene recognition. IEEE transactions on pattern analysis and machine intelligence, Vol. 32, 9 (2010), 1582--1596. Google ScholarDigital Library
- Jiang Wang, Yang Song, Thomas Leung, Chuck Rosenberg, Jingbin Wang, James Philbin, Bo Chen, and Ying Wu. 2014. Learning fine-grained image similarity with deep ranking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1386--1393. Google ScholarDigital Library
- Peng Wang, Lingqiao Liu, Chunhua Shen, Zi Huang, Anton van den Hengel, and Heng Tao Shen. 2017. Multi-attention network for one shot learning. In 2017 IEEE conference on computer vision and pattern recognition, CVPR. 22--25.Google Scholar
- Xiu-Shen Wei, Chen-Wei Xie, and Jianxin Wu. 2016. Mask-cnn: Localizing parts and selecting descriptors for fine-grained image recognition. arXiv preprint arXiv:1605.06878 (2016).Google Scholar
- Tianjun Xiao, Yichong Xu, Kuiyuan Yang, Jiaxing Zhang, Yuxin Peng, and Zheng Zhang. 2015. The application of two-level attention models in deep convolutional neural network for fine-grained image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 842--850.Google Scholar
- Guo-Sen Xie, Xu-Yao Zhang, Wenhan Yang, Ming-Liang Xu, Shuicheng Yan, and Cheng-Lin Liu. 2017. LG-CNN: From Local Parts to Global Discrimination for Fine-Grained Recognition. Pattern Recognition (2017).Google Scholar
- Bo Xiong and Kristen Grauman. 2016. Text detection in stores using a repetition prior. In Applications of Computer Vision (WACV), 2016 IEEE Winter Conference on. IEEE, 1--9.Google ScholarCross Ref
- Yichao Yan, Bingbing Ni, and Xiaokang Yang. 2017. Fine-Grained Recognition via Attribute-Guided Attentive Feature Aggregation. In Proceedings of the 2017 ACM on Multimedia Conference. ACM, 1032--1040. Google ScholarDigital Library
- Shulin Yang, Liefeng Bo, Jue Wang, and Linda G Shapiro. 2012. Unsupervised template learning for fine-grained object recognition. In Advances in neural information processing systems. 3122--3130. Google ScholarDigital Library
- Hantao Yao, Shiliang Zhang, Yongdong Zhang, Jintao Li, and Qi Tian. 2017. One-Shot Fine-Grained Instance Retrieval. In Proceedings of the 2017 ACM on Multimedia Conference. ACM, 342--350. Google ScholarDigital Library
- Erdem Yörük, Kaan Taha Öner, and Ceyhun Burak Akgül. 2016. An efficient Hough transform for multi-instance object recognition and pose estimation. In Pattern Recognition (ICPR), 2016 23rd International Conference on. IEEE, 1352--1357.Google ScholarCross Ref
- Ning Zhang, Jeff Donahue, Ross Girshick, and Trevor Darrell. 2014. Part-based R-CNNs for fine-grained category detection. In European conference on computer vision. Springer, 834--849.Google ScholarCross Ref
Index Terms
- Fine-Grained Grocery Product Recognition by One-Shot Learning
Recommendations
Fine-grained face verification
As performance on some aspects of the Labeled Faces in the Wild (LFW) benchmark approaches 100% accuracy, there is an intense debate on whether unconstrained face verification problem has already been solved. In this paper, we study a new face ...
Fine-Grained Product Class Recognition for Assisted Shopping
ICCVW '15: Proceedings of the 2015 IEEE International Conference on Computer Vision Workshop (ICCVW)Assistive solutions for a better shopping experience can improve the quality of life of people, in particular also of visually impaired shoppers. We present a system that visually recognizes the fine-grained product classes of items on a shopping list, ...
One-Shot 3D-Gradient Method Applied to Face Recognition
Progress in Pattern Recognition, Image Analysis, Computer Vision, and ApplicationsAbstractIn this work we describe a novel one-shot face recognition setup. Instead of using a 3D scanner to reconstruct the face, we acquire a single photo of the face of a person while a rectangular pattern is been projected over it. Using this unique ...
Comments