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A FWCL-based method for visual vocabulary formation

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Abstract

Traditional BOV (bag-of-visterms) constructing methods ignore different visual words’ contributions to image representation. Moreover, the size of visual vocabulary is generally set according to experience. Frequent weighted concept lattice (FWCL) is an interesting version of the concept lattice, and helps realize knowledge extraction in a more efficient way. In this paper, we present a dynamic refinement method for visual vocabulary formation based on frequency weighted concept lattice (FWCL) by using its characteristic of hierarchical data analysis and reduction. The original visual words’ weight value is assigned with information entropy, and FWCLs of each semantic category are constructed. According to the extent thresholds, each category of visual vocabulary is built and combined to generate the reduced global visual vocabulary for image representation. By adjusting the extent thresholds, different sizes of reduced visual vocabularies are dynamically extracted from this type of hierarchical structure. Lastly, experiments are carried on the two commonly used datasets, and experimental results show the effectiveness of our method on the scene classification.

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References

  1. Aswani Kumar C, Srinivas S (2010) Concept lattice reduction using fuzzy K-means clustering. Expert Syst Appl 37:2696–2704

    Article  Google Scholar 

  2. Bai S, Matsumoto T, Takeuchi Y et al (2013) Informative patches sampling for image classification by utilizing bottom-up and top-down information. Mach Vis Appl 24:959–970

    Article  Google Scholar 

  3. Blei M, Ng Y, Jordan I (2003) Latent dirchlet allocation. J Mach Learn Res 3:993–1022

    MATH  Google Scholar 

  4. Bo L, Ren X, Fox D (2011) Hierarchical matching pursuit for image classification: architecture and fast algorithms, in advances in neural information processing systems, pp 2115–2123

  5. Bosch A, Zisserman A, Munoz X (2006) Scene classification via plsa. In: proceedings of the 9th european conference on computer vision. Graz, Austria: Springer-Verlag, pp 517–530

  6. Bosch A, Zisserman A, Muoz X (2008) Scene classification using a hybrid generative/discriminative approach. IEEE Trans Pattern Anal Mach Intell 30:712–727

    Article  Google Scholar 

  7. Cai H, Yan F, Mikolajczyk K (2010) Learning weights for codebook in image classification and retrieval. In: IEEE international conference on computer vision and pattern recognition, pp 2320–2327

  8. Chen Y, Yao YY (2008) A multiview approach for intelligent data analysis based on data operators. Inf Sci 178:1–20

    Article  MathSciNet  MATH  Google Scholar 

  9. Cimiano A, Hotho S (2005) Staab, learning concept hierarchies from text corpora using formal concept analysis. J Artif Intell Res 24:305–339

    MATH  Google Scholar 

  10. de Campos T, Csurka G, Perronnin F (2012) Images as sets of locally weighted features. Comput Vis Image Underst 116:68–85

    Article  Google Scholar 

  11. Fan W, Bouguila N (2013) Variational learning of a dirichlet process of generalized dirichlet distributions for simultaneous clustering and feature selection. Pattern Recogn 46:2754–2769

    Article  Google Scholar 

  12. Fei-Fei L, Perona P (2005) A bayesian hierarchical model for learning natural scene categories. In: Proceedings of IEEE conference on computer vision and pattern recognition, San Diego, CA, pp 524–531

  13. Formica A (2006) Ontology-based concept similarity in formal concept analysis. Inf Sci 176:2624–2641

    Article  MathSciNet  MATH  Google Scholar 

  14. Fu Z, Lu H, Li W (2012) Incremental visual objects clustering with the growing vocabulary tree. Multimedia Tools Appl 56:535–552

    Article  Google Scholar 

  15. Gély A, Medina R, Nourine L (2009) Representing lattices using many-valued relations. Inf Sci 179:2729–2739

    Article  MATH  Google Scholar 

  16. Gosselin PH, Precioso F, Philipp-Foliguet S (2011) Incremental kernel learning for active image retrieval without global dictionaries. Pattern Recogn 44:2244–2254

    Article  Google Scholar 

  17. Gu G, Zhao Y, Zhu Z (2011) Integrated image representation based natural scene classification. Expert Syst Appl 38:11273–11279

    Article  Google Scholar 

  18. Hofmann T (2001) Unsupervised learning by probabilistic latent semantic analysis. J Mach Learn 41:177–196

    Article  Google Scholar 

  19. Hou J, Pelillo M (2013) A simple feature combination method based on dominant sets. Pattern Recogn 46:3129–3139

    Article  Google Scholar 

  20. Jiang Y, Ngo C, Yang J (2007) Towards optimal bag-of-features for object categorization and semantic video retrieval. In: Proceedings of the 6th ACM international conference on image and video retrieval, New York, USA, pp 494–501

  21. Kwon O, Kim J (2009) Concept lattices for visualizing and generating user profiles for context-aware service recommendations. Expert Syst Appl 36:1893–1902

    Article  Google Scholar 

  22. Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proceedings of IEEE conference on computer vision and pattern recognition, USA, pp 2169–2178

  23. Li LJ, Fei-Fei L (2010) Optimol: automatic online picture collection via incremental model learning. Int J Comput Vis 88:147–168

    Article  Google Scholar 

  24. Li T, Mei T, Kweon IS (2008) Learning optimal compact codebook for efficient object categorization. In: IEEE 2008 workshop on applications of computer vision, pp 1–6

  25. Liu J, Shah M (2007) Scene modeling using co-clustering. In: Proceedings of IEEE conference on computer vision and pattern recognition, Rio de Janeiro, Brazil, pp 298–304

  26. Liu L, Fieguth P, Clausi D, Kuang G (2012) Sorted random projections for robust rotation-invariant texture classification. Pattern Recogn 45:2405–2418

    Article  Google Scholar 

  27. Liu N, Dellandréa E, Chen L et al (2013) Multimodal recognition of visual concepts using histograms of textual concepts and selective weighted late fusion scheme. Comput Vis Image Underst 117:493–512

    Article  Google Scholar 

  28. Lowe D (2004) Distinctive image features from scale invariant keypoints. Int J Comput Vision 60:91–110

    Article  Google Scholar 

  29. Mallapragada P, Jin R, Jain A (2010) Online visual vocabulary pruning using pairwise constraints. In: IEEE international conference on computer vision and pattern recognition, pp 3073–3080

  30. Nowak E, Jurie F, Triggs B (2006) Sampling strategies for bag-of-features image classification. In: Proceedings of the 9th European conference on computer vision-volume part IV. Springer-Verlag, pp 490–503

  31. Quelhas P, Monay F, Odobez JM, Gatica-Perez D, Tuytelaars T (2007) A thousand words in a scene. IEEE Trans Pattern Anal Mach Intell 29:1575–1589

    Article  Google Scholar 

  32. Shi M, Xu R, Tao D et al (2013) W-tree indexing for fast visual word generation. IEEE Trans Image Process 22:1209–1222

    Article  MathSciNet  Google Scholar 

  33. Uijlings JRR, Smeulders AWM, Scha RJH (2012) The visual extent of an object: suppose we know the object locations. Int J Comput Vis 96:46–63

    Article  MathSciNet  Google Scholar 

  34. Stumme G, Taouil R, Bastide Y, Pasquier N, Lakha L (2002) Computing iceberg concept lattices with TITANIC. Data Knowl Eng 42:189–222

    Article  MATH  Google Scholar 

  35. Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival I (ed) Ordered sets. Reidel, Dordrecht, pp 415–470

    Google Scholar 

  36. Wu JX, Rehg JM (2009) Beyond the Euclidean distance: creating effective visual codebooks using the histogram intersection kernel. In: IEEE international conference on computer vision, pp 630–637

  37. Wu L, Hoi SCH, Yu N (2010) Semantics-preserving bag-of-words models and applications. IEEE Trans Image Process 19:1908–1920

    Article  MathSciNet  Google Scholar 

  38. Zhang SL, Guo P, Zhang JF (2011) Intent weight value acquisition of weighted concept lattice based on information entropy and deviance. Trans Beijing Inst Technol 1:59–63

    Google Scholar 

  39. Zhang S, Guo P, Zhang J, Wang X, Pedrycz W (2012) A completeness analysis of frequent weighted concept lattices and their algebraic properties. Data Knowl Eng 81-82:104–117

    Article  Google Scholar 

  40. Zhou L, Zhou Z, Hu D (2013) Scene classification using a multi-resolution bag-of-features model. Pattern Recogn 46:424–433

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially supported by the National Science Foundation of PR China (grant nos. 61373099, 60773014, 61073145, 58260910306052), the Natural Science Foundation of Shanxi Province, of P.R. China (grant no. 2010011021–2).

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Authors and Affiliations

  1. School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, 030024, China

    Sulan Zhang, Jifu Zhang & Meng Chu

  2. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Ping Guo

  3. Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, 36849-5347, USA

    Kai H. Chang

Authors
  1. Sulan Zhang
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  2. Jifu Zhang
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  3. Ping Guo
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  4. Meng Chu
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  5. Kai H. Chang
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Correspondence to Sulan Zhang.

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Zhang, S., Zhang, J., Guo, P. et al. A FWCL-based method for visual vocabulary formation. Multimed Tools Appl 75, 647–665 (2016). https://doi.org/10.1007/s11042-014-2313-7

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  • DOI: https://doi.org/10.1007/s11042-014-2313-7

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