FeatSet: A Compilation of Visual Features Extracted from Public Image Datasets
Resumo
In this paper, we present FeatSet, a compilation of visual features extracted from open image datasets reported in the literature. FeatSet has a collection of 11 visual features, consisting of color, texture, and shape representations of the images acquired from 13 datasets. We organized the available features in a standard collection, including the available metadata and labels, when available. We also provide a description of the domain of each dataset included in our collection, with visual analysis using Multidimensional Scaling (MDS) and Principal Components Analysis (PCA) methods. FeatSet is recommended for supervised and non-supervised learning, also widely supporting Content-Based Image Retrieval (CBIR) applications and complex data indexing using Metric Access Methods (MAMs).
Palavras-chave:
Visual feature, image, content-based retrieval, CBIR, machine learning, color, texture, shape
Referências
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Sikora, T. (2001). The mpeg-7 visual standard for content description-an overview. IEEE Transactions on Circuits and Systems for Video Technology, 11(6):696–702.
Stehling, R. O., Nascimento, M. A., and Falcão, A. X. (2002). A compact and efficient image retrieval approach based on border/interior pixel classification. In Proceedings of the 2002 ACM CIKM International Conference on Information and Knowledge Management, McLean, VA, USA, November 4-9, 2002, pages 102–109. ACM.
Yan, K., Wang, X., Lu, L., and Summers, R. M. (2017). Deeplesion: Automated deep mining, categorization and detection of significant radiology image findings using large-scale clinical lesion annotations.CoRR, abs/1710.01766.
Yang, L., Luo, P., Loy, C. C., and Tang, X. (2015). A large-scale car dataset for fine-grained categorization and verification. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015, pages 3973–3981. IEEE Computer Society.
Zabot, G. F., Cazzolato, M. T., Scabora, L. C., Faical, B. S., Traina, A. J. M., and Traina Jr., C. (2019a). UCORM: indexing uncorrelated metric spaces for concise content-based retrieval of medical images. In 32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019, Cordoba, Spain, June 5-7, 2019, pages 306–311. IEEE.
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Bedo, M. V. N., Blanco, G., Oliveira, W. D., Cazzolato, M. T., Costa, A. F., Rodrigues-Jr., J. F., Traina, A. J. M., and Traina Jr., C. (2015). Techniques for effective and efficient fire detection from social media images. In Hammoudi, S., Maciaszek, L. A., and Teniente, E., editors, ICEIS 2015 - Proceedings of the 17th International Conference on Enterprise Information Systems, Volume 1, Barcelona, Spain, 27-30 April, 2015, pages 34–45. SciTePress.
Borg, I. and Groenen, P. (2005).Modern Multidimensional Scaling: Theory and Applications. Springer Series in Statistics. Springer New York.
Cazzolato, M. T., Avalhais, L. P. S., Chino, D. Y. T., Ramos, J. S., Souza, J. A., Rodrigues-Jr, J. F., and Traina, A. J. M. (2017). Fismo: A compilation of datasets from emergency situations for fire and smoke analysis. In SBBD2017 - SBBD Proceedings of Satellite Events of the 32nd Brazilian Symposium on Databases - DSW (Dataset Showcase Workshop), pages 213–223. SBC.
Chino, D. Y. T., Avalhais, L. P. S., Rodrigues-Jr., J. F., and Traina, A. J. M. (2015). Bowfire: Detection of fire in still images by integrating pixel color and texture analysis. In 28th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2015, Salvador, Bahia, Brazil, August 26-29, 2015, pages 95–102. IEEE Computer Society.
Cohen, J. P., Morrison, P., Dao, L., Roth, K., Duong, T. Q., and Ghassemi, M. (2020). Covid-19 image data collection: Prospective predictions are the future. CoRR, abs/2006.11988.
de Sousa Fogaça, I. C. O. and Bueno, R. (2020). Temporal evolution of complex data. In Anais do XXXV Simpósio Brasileiro de Bancos de Dados, SBBD 2020, online, September 28 - October 1, 2020, pages 25–36. SBC.
Hajder, S. (2020).Letters organized by typefaces. Last accessed in October, 2020.
Kasutani, E. and Yamada, A. (2001). The mpeg-7 color layout descriptor: a compact image feature description for high-speed image/video segment retrieval. In Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), volume 1, pages 674–677 vol. 1.
Khosla, A., Jayadevaprakash, N., Yao, B., and Fei-Fei, L. (2011). Novel dataset for fine-grained image categorization. InFirst Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO.
Krause, J., Stark, M., Deng, J., and Fei-Fei, L. (2013). 3d object representations for fine-grained categorization. In 2013 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2013, Sydney, Australia, December 1-8, 2013, pages 554–561. IEEE Computer Society.
Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278–2324.
Lee, K.-L. and Chen, L.-H. (2005). An efficient computation method for the texture browsing descriptor of mpeg-7. Image and Vision Computing, 23:479–489.
Maheshwari, S., Sharma, R. R., and Kumar, M. (2021). Lbp-based information assisted intelligent system for COVID-19 identification.Comput. Biol. Medicine, 134:104453.
Manjunath, B., Ohm, J., Vasudevan, V., and Yamada, A. (2001). Color and texture descriptors. Circuits and Systems for Video Technology, IEEE Transactions on, 11:703 –715.
Manjunath, B. S., Salembier, P., and Sikora, T. (2002).Introduction to MPEG-7: multimedia content description interface. John Wiley & Sons.
Moriyama, A., Rodrigues, L. S., Scabora, L. C., Cazzolato, M. T., Traina, A. J. M., and Traina, C. (2021). Vd-tree: how to build an efficient and fit metric access method using voronoi diagrams. In Hung, C., Hong, J., Bechini, A., and Song, E., editors,SAC ’21: The 36th ACM/SIGAPP Symposium on Applied Computing, Virtual Event, Republic of Korea, March 22-26, 2021, pages 327–335. ACM.
Nene, S. A., Nayar, S. K., and Murase, H. (2020). Columbia object image library (coil-100). Technical report, Technical Report CUCS-006-96. Last accessed in October, 2020.
Oliveira, P. H., Scabora, L. C., Cazzolato, M. T., Bedo, M. V. N., Traina, A. J. M., and Traina-Jr., C. (2017). MAMMOSET: An Enhanced Dataset of Mammograms. In Proceedings of the Satellite Events of the 32nd Brazilian Symposium on Databases, pages 256–266. SBC.
Park, D. K., Jeon, Y. S., and Won, C. S. (2000). Efficient use of local edge histogram descriptor. In Ghandeharizadeh, S., Chang, S., Fischer, S., Konstan, J. A., and Nahrstedt, K., editors, Proceedings of the ACM Multimedia 2000 Workshops, Los Angeles, CA, USA, October 30 - November 3, 2000, pages 51–54. ACM Press.
Pereira, J. W. and Ribeiro, M. X. (2021). Semantic annotation and classification of mammography images using ontologies. In Almeida, J. R., Gonz ́alez, A. R., Shen, L., Kane, B., Traina, A., Soda, P., and Oliveira, J. L., editors, 34th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2021, Aveiro, Portugal, June7-9, 2021, pages 378–383. IEEE.
Sikora, T. (2001). The mpeg-7 visual standard for content description-an overview. IEEE Transactions on Circuits and Systems for Video Technology, 11(6):696–702.
Stehling, R. O., Nascimento, M. A., and Falcão, A. X. (2002). A compact and efficient image retrieval approach based on border/interior pixel classification. In Proceedings of the 2002 ACM CIKM International Conference on Information and Knowledge Management, McLean, VA, USA, November 4-9, 2002, pages 102–109. ACM.
Yan, K., Wang, X., Lu, L., and Summers, R. M. (2017). Deeplesion: Automated deep mining, categorization and detection of significant radiology image findings using large-scale clinical lesion annotations.CoRR, abs/1710.01766.
Yang, L., Luo, P., Loy, C. C., and Tang, X. (2015). A large-scale car dataset for fine-grained categorization and verification. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015, pages 3973–3981. IEEE Computer Society.
Zabot, G. F., Cazzolato, M. T., Scabora, L. C., Faical, B. S., Traina, A. J. M., and Traina Jr., C. (2019a). UCORM: indexing uncorrelated metric spaces for concise content-based retrieval of medical images. In 32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019, Cordoba, Spain, June 5-7, 2019, pages 306–311. IEEE.
Zabot, G. F., Cazzolato, M. T., Scabora, L. C., Traina, A. J. M., and Traina Jr., C. (2019b). Efficient indexing of multiple metric spaces with spectra. In IEEE International Symposium on Multimedia, ISM 2019, San Diego, CA, USA, December 9-11, 2019, pages169–176. IEEE.
Publicado
04/10/2021
Como Citar
CAZZOLATO, Mirela T.; SCABORA, Lucas C.; ZABOT, Guilherme F.; GUTIERREZ, Marco A.; TRAINA JR., Caetano; TRAINA, Agma J. M..
FeatSet: A Compilation of Visual Features Extracted from Public Image Datasets. In: DATASET SHOWCASE WORKSHOP (DSW), 3. , 2021, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 89-100.
DOI: https://doi.org/10.5753/dsw.2021.17417.
