Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval
Resumo
Nesse artigo, nós apresentamos o Hierarchy-of-Visual-Words (HoVW), um novo método para recuperação de logotipos (RL) que decompõe as imagens em formas geométricas mais simples e define um descritor para a representação logotipos binários através da codificação de suas formas-componente em arranjos hierárquicos. A organização hierárquica de informação visual proposta armazena cada forma-componente como uma palavra visual. Ela é capaz de representar a geometria de cada elemento individualmente e a topologia de cada logotipo, tornando o descritor robusto contra transformações lineares e, em algum grau, transformações não lineares. Experimentos mostram que o HoVW supera outros métodos de RL nas bases de imagens MPEG-7 CE-1 e MPEG-7 CE-2.
Palavras-chave:
Recuperação de logotipos, Extração e comparação de características visuais, Abordagem baseada em aprendizado
Referências
C. Hung Wei, Y. Li, W. Y. Chau, and C. T. Li, “Trademark image retrieval using synthetic features for describing global shape and interior structure,” Pattern Recognit., vol. 42, pp. 386–394, 2009.
F. M. Anuar, R. Setchi, and Y. Lai, “Trademark image retrieval using an integrated shape descriptor,” Expert Syst. Appl. , vol. 40, pp. 105–121, 2013.
H. Qi, K. Li, Y. Shen, and W. Qu, “An effective solution for trademark image retrieval by combining shape description and feature matching,” Pattern Recognit., vol. 43, pp. 2017–2027, 2010.
F. Liu, B. Wang, and F. Zeng, “Trademark image retrieval using hierarchical region feature description,” in Proc. IEEE Intl. Conf. Image Process., 2017, pp. 3620–3624.
P. Sidiropoulos, S. Vrochidis, and I. Kompatsiaris, “Content-based binary image retrieval using the adaptive hierarchical density histogram,” Pattern Recognit. , vol. 44, pp. 739–750, 2011.
M. Yang, G. Qiu, J. Huang, and D. Elliman, “Near-duplicate image recognition and content-based image retrieval using adaptive hierarchical geometric centroids,” in Proc. Intl. Conf. Pattern Recognit., 2006, pp. 958–961.
N. Alajlan, M. S. Kamel, and G. Freeman, “Multi-object image retrieval based on shape and topology,” Signal Process. Image Commun., vol. 21, pp. 904–918, 2006.
N. Alajlan, M. S. Kamel, and G. H. Freeman, “Geometry-based image retrieval in binary image databases,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, pp. 1003–1013, 2008.
I. Biederman, “Recognition-by-components: a theory of human image understanding,” Psychol. Rev. , vol. 94, pp. 115–147, 1987.
M. Pawlik and N. Augsten, “Tree edit distance: robust and memory-efficient,” Inf. Syst., vol. 56, pp. 157–173, 2016.
S. Lloyd, “Least squares quantization in PCM,” IEEE Trans. Inf. Theor. ,vol. 28, pp. 129–137, 1982.
D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 24, pp. 603–619, 2002.
M. Pelillo, K. Siddiqi, and S. W. Zucker, “Matching hierarchical structures using association graphs,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, pp. 1105–1120, 1999.
F. B. Silva, S. Goldenstein, S. Tabbone, and R. S. Torres, “Image classification based on bag of visual graphs,” in Proc. IEEE Intl. Conf. Image Process., 2013, pp. 4312–4316.
F. B. Silva, R. O. Werneck, S. Goldenstein, S. Tabbone, and R. S. Torres, “Graph-based bag-of-words for classification,” Pattern Recognit., vol. 74, pp. 266–285, 2017.
T. Huang, G. Yang, and G. Tang, “A fast two-dimensional median filtering algorithm,” IEEE Trans. Acoust., Speech, Signal Process. , vol. 27, pp. 13–18, 1979.
C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proc. Intl. Conf. Comput. Vis., 1998, pp. 839–846.
N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man Cybern., vol. 9, pp. 62–66, 1979.
S. Suzuki and K. Abe, “Topological structural analysis of digitized binary images by border following,” Comput. Vis. Graph. Image Process., vol. 30, pp. 32–46, 1985.
A. Khotanzad and Y.H. Hong, “Invariant image recognition by Zernike moments,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 12, pp. 489–497, 1990.
Y. Mingqiang, K. Kidiyo, and R. Joseph, “A survey of shape feature extraction techniques,” in Pattern Recognition , P. Yin, Ed., chapter 3. IntechOpen, 2008.
I. T. Young, J. E. Walker, and J. E. Bowie, “An analysis technique for biological shape. i*,” Inf. Control, vol. 25, pp. 357–370, 1974.
L. Liu and M. T. Ozsu, “Encyclopedia of Database Systems”, Springer US, 2009.
F. M. Anuar, R. Setchi, and Y. Lai, “Trademark image retrieval using an integrated shape descriptor,” Expert Syst. Appl. , vol. 40, pp. 105–121, 2013.
H. Qi, K. Li, Y. Shen, and W. Qu, “An effective solution for trademark image retrieval by combining shape description and feature matching,” Pattern Recognit., vol. 43, pp. 2017–2027, 2010.
F. Liu, B. Wang, and F. Zeng, “Trademark image retrieval using hierarchical region feature description,” in Proc. IEEE Intl. Conf. Image Process., 2017, pp. 3620–3624.
P. Sidiropoulos, S. Vrochidis, and I. Kompatsiaris, “Content-based binary image retrieval using the adaptive hierarchical density histogram,” Pattern Recognit. , vol. 44, pp. 739–750, 2011.
M. Yang, G. Qiu, J. Huang, and D. Elliman, “Near-duplicate image recognition and content-based image retrieval using adaptive hierarchical geometric centroids,” in Proc. Intl. Conf. Pattern Recognit., 2006, pp. 958–961.
N. Alajlan, M. S. Kamel, and G. Freeman, “Multi-object image retrieval based on shape and topology,” Signal Process. Image Commun., vol. 21, pp. 904–918, 2006.
N. Alajlan, M. S. Kamel, and G. H. Freeman, “Geometry-based image retrieval in binary image databases,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, pp. 1003–1013, 2008.
I. Biederman, “Recognition-by-components: a theory of human image understanding,” Psychol. Rev. , vol. 94, pp. 115–147, 1987.
M. Pawlik and N. Augsten, “Tree edit distance: robust and memory-efficient,” Inf. Syst., vol. 56, pp. 157–173, 2016.
S. Lloyd, “Least squares quantization in PCM,” IEEE Trans. Inf. Theor. ,vol. 28, pp. 129–137, 1982.
D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 24, pp. 603–619, 2002.
M. Pelillo, K. Siddiqi, and S. W. Zucker, “Matching hierarchical structures using association graphs,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, pp. 1105–1120, 1999.
F. B. Silva, S. Goldenstein, S. Tabbone, and R. S. Torres, “Image classification based on bag of visual graphs,” in Proc. IEEE Intl. Conf. Image Process., 2013, pp. 4312–4316.
F. B. Silva, R. O. Werneck, S. Goldenstein, S. Tabbone, and R. S. Torres, “Graph-based bag-of-words for classification,” Pattern Recognit., vol. 74, pp. 266–285, 2017.
T. Huang, G. Yang, and G. Tang, “A fast two-dimensional median filtering algorithm,” IEEE Trans. Acoust., Speech, Signal Process. , vol. 27, pp. 13–18, 1979.
C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proc. Intl. Conf. Comput. Vis., 1998, pp. 839–846.
N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man Cybern., vol. 9, pp. 62–66, 1979.
S. Suzuki and K. Abe, “Topological structural analysis of digitized binary images by border following,” Comput. Vis. Graph. Image Process., vol. 30, pp. 32–46, 1985.
A. Khotanzad and Y.H. Hong, “Invariant image recognition by Zernike moments,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 12, pp. 489–497, 1990.
Y. Mingqiang, K. Kidiyo, and R. Joseph, “A survey of shape feature extraction techniques,” in Pattern Recognition , P. Yin, Ed., chapter 3. IntechOpen, 2008.
I. T. Young, J. E. Walker, and J. E. Bowie, “An analysis technique for biological shape. i*,” Inf. Control, vol. 25, pp. 357–370, 1974.
L. Liu and M. T. Ozsu, “Encyclopedia of Database Systems”, Springer US, 2009.
Publicado
28/10/2019
Como Citar
LOURENÇO, Vítor N.; SILVA, Gabriela G.; FERNANDES, Leandro A. F..
Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval. In: WORKSHOP DE TRABALHOS DA GRADUAÇÃO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 32. , 2019, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 211-214.
DOI: https://doi.org/10.5753/sibgrapi.est.2019.8332.
