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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Learning Visual Word Patterns Using BoVW Model for Image Retrieval

verfasst von : P. Arulmozhi, S. Abirami

Erschienen in: Computational Intelligence in Data Mining

Verlag: Springer Singapore

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Abstract

Bag of visual words (BoVW) model is popularly used for retrieving relevant images for a requested image. Though it is simple, compact, efficient, and scalable image representation, one of its major drawbacks is the visual words formed by this model are noisy that leads to mismatched visual words between two semantically irrelevant images and thus the discriminative power gets reduced. In this paper, a new pattern is learnt from the generated visual words for each image category (group) and the learnt pattern is applied to the numerous images of each category. The uniqueness and correctness of the learnt pattern are verified leading to the reduction of false image matches. This pattern learning is experimented using Caltech 256 dataset and obtained higher precision values.

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Vorheriges Kapitel Face Biometric-Based Document Image Retrieval Using SVD Features
Nächstes Kapitel Test Scenario Prioritization Using UML Use Case and Activity Diagram
Literatur
1.
Datta R, Joshi D, Li J, Wang JZ. Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys (CSUR). (2008) 40(2):5.
2.
Sivic J, Zisserman A. Video Google: A text retrieval approach to object matching in videos. In Computer Vision, Proceedings. Ninth IEEE International Conference (2003), 1470–1477.
3.
Philbin J, Chum O, Isard M, Sivic J, Zisserman A: Lost in quantization: Improving particular object retrieval in large scale image databases. In Computer Vision and Pattern Recognition, CVPR 2008. IEEE Conference (2008), 1–8.
4.
Wu Z, Ke Q, Isard M, Sun J.,: Bundling features for large scale partial-duplicate web image search. In Computer Vision and Pattern Recognition, CVPR, (2009), 25–32.
5.
Philbin J, Chum O, Isard M, Sivic J, Zisserman A., Object retrieval with large vocabularies and fast spatial matching. In Computer Vision and Pattern Recognition. (2007), 1–8.
6.
Yang YH, Wu PT, Lee CW, Lin KH, Hsu WH, Chen HH.,: ContextSeer: context search and recommendation at query time for shared consumer photos. In Proceedings of the 16th ACM international conference, (2008), 199–208.
7.
Turcot P, Lowe D.,: Better matching with fewer features: The selection of useful features in large database recognition problems. In ICCV workshop, (2009).
8.
Perronnin F, Dance C, Csurka G, Bressan M.,: Adapted vocabularies for generic visual categorization. In Computer Vision–ECCV, (2006), 464–475.
9.
Moosmann, F., Triggs, W.,: Randomized clustering forests for building fast and discriminative visual vocabularies. Article in Advances in neural information processing systems, (2006).
10.
Van Gemert, J. C., Veenman, C. J., Geusebroek, J. M.,: Visual word ambiguity. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32(7), (2010), 1271–1283.
11.
Xie, L., Wang, J., Zhang, B., & Tian, Q.,: Incorporating visual adjectives for image classification. Neurocomputing, (2015).
12.
Chen, Y., Dick, A., Li, X., & Van Den Hengel, A., Spatially aware feature selection and weighting for object retrieval. Image and Vision Computing, 31(12), (2013) 935–948.
13.
Wang, J., Li, Y., Zhang, Y., Wang, C., Xie, H., Chen, G., Gao, X.,: Bag-of-features based medical image retrieval via multiple assignment and visual words weighting. IEEE transactions on medical imaging, 30(11), (2011), 1996–2011.
14.
Jinliang Yao, Bing Yang, and Qiuming Zhu,: Near-Duplicate Image Retrieval Based on Contextual Descriptor, IEEE Signal Processing Letters, . 22, No. 9, (2015), 1404–1408.
15.
Yang, Y. B., Zhu, Q. H., & Pan, L. Y.,: Visual feature coding for image classification integrating dictionary structure. Pattern Recognition, 48(10), (2015), 3067–3075.
16.
Zhou, W., Li, H., Hong, R., and Tian, Q.,: BSIFT: toward data-independent codebook for large scale image search. Image Processing, IEEE Transactions, 24(3), (2015), 967–979.
17.
Zhao, H., Wang, Z., Liu, P., & Wu, B.,: A fast binary encoding mechanism for approximate nearest neighbor search. Neurocomputing, 178, (2016), 112–122.
18.
Zhen Liu, Houqiang Li, Wengang Zhou, and Qi Tian,: Contextual hashing for the large scale Image search, IEEE Transactions on Image Processing, . 23, No. 4, (2014), 1606–1614.
19.
Liu, Z., Chen, S., Bu, S., & Li, K.,: High-level semantic feature for 3D shape based on deep belief networks. In Multimedia and Expo (ICME), IEEE International Conference (2014).
20.
Li, Q., Li, K., You, X., Bu, S., & Liu, Z.,: Place recognition based on deep feature and adaptive weighting of similarity matrix. Neurocomputing, 199, (2016), 114–127.
21.
Guo, Yanming, Yu Liu, Ard Oerlemans, Songyang Lao, Song Wu, and Michael S. Lew.,: Deep learning for visual understanding: A review. Neurocomputing (2015).
22.
Lowe, D. G.,: Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), (2004), 91–110.
23.
Griffin, G., A. Holub, and P. Perona.: Caltech-256 object category dataset California Inst. Technol., Tech. Rep. 7694, (2007).
24.
Vedaldi, A., & Fulkerson, B.,: VLFeat: An open and portable library of computer vision algorithms (2008).
Metadaten
Titel
Learning Visual Word Patterns Using BoVW Model for Image Retrieval
verfasst von
P. Arulmozhi
S. Abirami
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Computational Intelligence in Data Mining

Print ISBN: 978-981-10-3873-0

Electronic ISBN: 978-981-10-3874-7

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-3874-7_46