FeatSet+: Visual Features Extracted from Public Image Datasets
DOI:
https://doi.org/10.5753/jidm.2022.2328Keywords:
Dataset, image, visual features, color, texture, shape, CBIR, retrieval, analysisAbstract
Real-world applications generate large amounts of images every day. With the generalized use of social media, users frequently share images acquired by smartphones. Also, hospitals, clinics, exhibits, factories, and other facilities generate images with potential use for many applications. Processing the generated images usually requires feature extraction, which can be time-consuming and laborious. In this paper, we present FeatSet+, a compilation of color, texture and shape visual features extracted from 17 open image datasets reported in the literature. FeatSet+ provides a collection of 11 distinct visual features, extracted by well-known Feature Extraction Methods (FEMs) such as LBP, Haralick, and Color Layout. We organized the available features in a standard collection, including the metadata and labels, when available. Eleven of the datasets also contain classes, which aid the evaluation of supervised methods such as classifiers and clustering tasks. FeatSet+ is available for download in a public repository as sql scripts and csv files. Additionally, FeatSet+ provides a description of the domain of each dataset, including the reference to the original work and link. We show the potential applicability of FeatSet+ in four computational tasks: multi-attribute analysis and retrieval, visual analysis using Multidimensional Scaling (MDS) and Principal Components Analysis (PCA), global feature classification, and dimensionality reduction. FeatSet+ can be employed to evaluate supervised and non-supervised learning tasks, also widely supporting Content-Based Image Retrieval (CBIR) applications and complex data indexing using Metric Access Methods (MAMs).
Downloads
References
Bastos, I. L. O., Angelo, M. F., and Loula, A. C. Recognition of static gestures applied to brazilian sign language (Libras). In 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images. pp. 305–312, 2015. DOI: 10.1109/SIBGRAPI.2015.26.
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. Techniques for effective and efficient fire detection from social media images. In ICEIS 2015 - Proceedings of the 17th International Conference on Enterprise Information Systems, Volume 1, Barcelona, Spain, 27-30 April, 2015, S. Hammoudi, L. A. Maciaszek, and E. Teniente (Eds.). SciTePress, pp. 34–45, 2015. DOI:10.5220/0005341500340045.
Borg, I. and Groenen, P. Modern Multidimensional Scaling: Theory and Applications. Springer Series in Statistics. Springer New York, 2005. ISBN: 9780387251509.
Cazzolato, M., Scabora, L. C., Zabot, G. F., Gutierrez, M. A., Traina-Jr., C., and Traina, A. J. M. FeatSet: A compilation of visual features extracted from public image datasets. In Anais do III Dataset Showcase Workshop. SBC, Porto Alegre, RS, Brasil, pp. 89–100, 2021. DOI: 10.5753/dsw.2021.17417.
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. 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). SBC, Uberlandia, Brazil, pp. 213–223, 2017. ISBN: 978-85-7669-399-4.URL: [link].
Cazzolato, M. T., Bedo, M. V. N., Costa, A. F., de Souza, J. A., Jr., C. T., Jr., J. F. R., and Traina, A. J. M. Unveiling smoke in social images with the smokeblock approach. In Proceedings of the 31st Annual ACM Symposium on Applied Computing, Pisa, Italy, April 4-8, 2016, S. Ossowski (Ed.). ACM, pp. 49–54, 2016. DOI: 10.1145/2851613.2851634.
Chino, D. Y. T., Avalhais, L. P. S., Rodrigues-Jr., J. F., and Traina, A. J. M. 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. IEEE Computer Society, pp. 95–102, 2015. DOI: 10.1109/SIBGRAPI.2015.19.
Cohen, J. P., Morrison, P., Dao, L., Roth, K., Duong, T. Q., and Ghassemi, M. Covid-19 image data collection: Prospective predictions are the future. CoRR vol. abs/2006.11988, 2020. URL: [link].
de Sousa Fogaça, I. C. O. and Bueno, R. Temporal evolution of complex data. In Anais do XXXV Simpósio Brasileiro de Bancos de Dados, SBBD 2020, online, September 28 - October 1, 2020. SBC, pp. 25–36, 2020. DOI: 10.5753/sbbd.2020.13622.
Hajder, S. Letters organized by typefaces, 2020. Last accessed in October, 2020. URL: [link].
Kasutani, E. and Yamada, A. 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). Vol. 1. pp. 674–677 vol.1, 2001. DOI: 10.1109/ICIP.2001.959135.
Khosla, A., Jayadevaprakash, N., Yao, B., and Fei-Fei, L. Novel dataset for fine-grained image categorization. In First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, CO, 2011. URL: [link].
Krause, J., Stark, M., Deng, J., and Fei-Fei, L. 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. IEEE Computer Society, pp. 554–561, 2013. DOI: 10.1109/ICCVW.2013.77.
Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86 (11): 2278–2324, 1998. DOI: 10.1109/5.726791.
Lee, K.-L. and Chen, L.-H. An efficient computation method for the texture browsing descriptor of mpeg-7. Image and Vision Computing vol. 23, pp. 479–489, 05, 2005. DOI: 10.1016/j.imavis.2004.12.002.
Maheshwari, S., Sharma, R. R., and Kumar, M. LBP-based information assisted intelligent system for COVID-19 identification. Comput. Biol. Medicine vol. 134, pp. 104453, 2021. DOI: 10.1016/j.compbiomed.2021.104453.
Manjunath, B., Ohm, J., Vasudevan, V., and Yamada, A. Color and texture descriptors. Circuits and Systems for Video Technology, IEEE Transactions on vol. 11, pp. 703 – 715, 07, 2001. DOI: 10.1109/76.927424.
Manjunath, B. S., Salembier, P., and Sikora, T. Introduction to MPEG-7: multimedia content description interface. John Wiley & Sons, 2002. ISBN: 978-0-471-48678-7.
Moriyama, A., Rodrigues, L. S., Scabora, L. C., Cazzolato, M. T., Traina, A. J. M., and Traina, C. Vd-tree: how to build an efficient and fit metric access method using voronoi diagrams. In SAC ’21: The 36th ACM/SIGAPP Symposium on Applied Computing, Virtual Event, Republic of Korea, March 22-26, 2021, C. Hung, J. Hong, A. Bechini, and E. Song (Eds.). ACM, pp. 327–335, 2021. DOI: 10.1145/3412841.3441915.
Nene, S. A., Nayar, S. K., and Murase, H. Columbia object image library (COIL-100). Tech. rep., Technical Report CUCS-006-96, 2020. 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. MAMMOSET: An Enhanced Dataset of Mammograms. In Proceedings of the Satellite Events of the 32nd Brazilian Symposium on Databases. SBC, pp. 256–266, 2017. URL: [link].
Oliveira, P. H., Scabora, L. C., Cazzolato, M. T., Oliveira, W. D., Paixão, R. S., Traina, A. J. M., and Traina, C. Employing domain indexes to efficiently query medical data from multiple repositories. IEEE J. Biomed. Health Informatics 23 (6): 2220–2229, 2019. DOI: 10.1109/JBHI.2018.2881381.
Park, D. K., Jeon, Y. S., and Won, C. S. Efficient use of local edge histogram descriptor. In Proceedings of the ACM Multimedia 2000 Workshops, Los Angeles, CA, USA, October 30 - November 3, 2000, S. Ghandeharizadeh, S. Chang, S. Fischer, J. A. Konstan, and K. Nahrstedt (Eds.). ACM Press, pp. 51–54, 2000. DOI: 10.1145/357744.357758.
Pereira, J. W. and Ribeiro, M. X. Semantic annotation and classification of mammography images using ontologies. In 34th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2021, Aveiro, Portugal, June 7-9, 2021, J. R. Almeida, A. R. González, L. Shen, B. Kane, A. Traina, P. Soda, and J. L. Oliveira (Eds.). IEEE, pp. 378–383, 2021. DOI: 10.1109/CBMS52027.2021.00043.
Rodrigues, L. S., Cazzolato, M. T., Traina, A. J. M., and Traina, C. Taking advantage of highly-correlated attributes in similarity queries with missing values. In Similarity Search and Applications - 13th International Conference, SISAP 2020, Copenhagen, Denmark, September 30 - October 2, 2020, Proceedings, S. Satoh, L. Vadicamo, A. Zimek, F. Carrara, I. Bartolini, M. Aumüller, B. Þ. Jónsson, and R. Pagh (Eds.). Lecture Notes in Computer Science, vol. 12440. Springer, pp. 168–176, 2020. DOI: 10.1007/978-3-030-60936-8_13.
Sikora, T. The mpeg-7 visual standard for content description-an overview. IEEE Transactions on Circuits and Systems for Video Technology 11 (6): 696–702, 2001. DOI: 10.1109/76.927422.
Simonyan, K. and Zisserman, A. Very deep convolutional networks for large-scale image recognition. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Y. Bengio and Y. LeCun (Eds.), 2015.
Singla, A., Yuan, L., and Ebrahimi, T. Food/non-food image classification and food categorization using pre-trained googlenet model. In Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management. MADiMa ’16. Association for Computing Machinery, New York, NY, USA, pp. 3–11, 2016. DOI: 10.1145/2986035.2986039.
Stehling, R. O., Nascimento, M. A., and Falcão, A. X. 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. ACM, pp. 102–109, 2002. DOI: 10.1145/584792.584812.
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S. The caltech-ucsd birds-200-2011 dataset. Tech. Rep. CNS-TR-2011-001, California Institute of Technology, 2011.
Xian, Y., Lampert, C. H., Schiele, B., and Akata, Z. Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. IEEE Trans. Pattern Anal. Mach. Intell. 41 (9): 2251–2265, 2019. DOI: 10.1109/TPAMI.2018.2857768.
Yan, K., Wang, X., Lu, L., and Summers, R. M. DeepLesion: Automated deep mining, categorization and detection of significant radiology image findings using large-scale clinical lesion annotations. CoRR vol. abs/1710.01766, 2017. URL: [link].
Yang, L., Luo, P., Loy, C. C., and Tang, X. 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. IEEE Computer Society, pp. 3973–3981, 2015. DOI: 10.1109/CVPR.2015.7299023.
Zabot, G. F., Cazzolato, M. T., Scabora, L. C., Faiçal, B. S., Traina, A. J. M., and Traina Jr., C. 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. IEEE, pp. 306–311, 2019. DOI: 10.1109/CBMS.2019.00070.
Zabot, G. F., Cazzolato, M. T., Scabora, L. C., Traina, A. J. M., and Traina Jr., C. Efficient indexing of multiple metric spaces with spectra. In IEEE International Symposium on Multimedia, ISM 2019, San Diego, CA, USA, December 9-11, 2019. IEEE, pp. 169–176, 2019. DOI: 10.1109/ISM46123.2019.00038.
