Fusión temprana de descriptores extraídos de mapas de prominencia multi-nivel para clasificar imágenes

  1. Fidalgo, E. 1
  2. Alegre, E. 1
  3. Fernández-Robles, L. 1
  4. González-Castro, V. 1
  1. 1 Universidad de León

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Year of publication: 2019

Volume: 16

Issue: 3

Pages: 358-368

Type: Article

DOI: 10.4995/RIAI.2019.10640 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista iberoamericana de automática e informática industrial ( RIAI )


In this paper, we propose a method that improves the classification of images. Considering saliency maps as if they were topographic maps and filtering the characteristics of the image’s background, the Bag of VisualWords (BoVW) coding is improved. First, we evaluated six known algorithms to generate saliency maps and we selected GBVS and SIM because they are the ones that retain most of the information of the object. Next, we eliminated the extracted SIFT descriptors belonging to the background by filtering features based on binary images obtained at various levels of the selected saliency maps. We filtered the descriptors by obtaining layers at various levels of the saliency maps, and we evaluated the early fusion of the SIFT descriptors contained in these layers into five dierent datasets. The results obtained indicate that the proposed method always improves the reference method when combining the first two layers of GBVS or SIM and the dataset contains images with a single object.

Bibliographic References

  • Al-khafaji, S. L., Zhou, J., Zia, A., Liew, A. W. C., Feb 2018. Spectral-spatial scale invariant feature transform for hyperspectral images. IEEE Transactions on Image Processing 27 (2), 837-850. https://doi.org/10.1109/TIP.2017.2749145
  • Al-Nabki, W., Fidalgo, E., Alegre, E., De Paz, I., 2017. Classifying Illegal Activities on Tor Network Based on Web Textual Contents. 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference 1, 35-43. https://doi.org/10.18653/v1/E17-1004
  • Beucher, S., Lantuejoul, C., 1979. Use of Watersheds in Contour Detection.
  • Biagio, M. S., Bazzani, L., Cristani, M., Murino, V., oct 2014. Weighted bag of visual words for object recognition. In: 2014 IEEE International Conference on Image Processing, ICIP 2014. IEEE, pp. 2734-2738. https://doi.org/10.1109/ICIP.2014.7025553
  • Biswas, R., Fidalgo, E., Alegre, E., 2017. Recognition of Service Domains on TOR Dark Net using Perceptual Hashing and Image Classification Techniques. 8th International Conference on Imaging for Crime Detection and Prevention 2017 (5). https://doi.org/10.1049/ic.2017.0041
  • Borji, A., Itti, L., jan 2013. State-of-the-art in visual attention modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (1), 185-207. https://doi.org/10.1109/TPAMI.2012.89
  • Cervantes, J., Taltempa, J., Garcíaa-Lamont, F., Castilla, J. S. R., Rendon, A. Y., Jalili, L. D., 2017. Análisis comparativo de las técnicas utilizadas en un sistema de reconocimiento de hojas de planta. Revista Iberoamericana de Automática e Informática Industrial RIAI 14 (1), 104 -114. https://doi.org/10.1016/j.riai.2016.09.005
  • Chatzichristofis, S. A., Iakovidou, C., Boutalis, Y., Marques, O., feb 2013. Co.Vi.Wo.: Color visual words based on non-predefined size codebooks. IEEE Transactions on Cybernetics 43 (1), 192-205. https://doi.org/10.1109/TSMCB.2012.2203300
  • Chaves, D., Saikia, S., Fernández-Robles, L., Alegre, E., Trujillo, M., 2018. A Systematic Review on Object Localisation Methods in Images. Revista Iberoamericana de Automática e Informática Industrial 15, 231-242. https://doi.org/10.4995/riai.2018.10229
  • Chen, J., Feng, B., Xu, B., 2014a. Spatial similarity measure of visual phrases for image retrieval. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8326 LNCS. Springer, Cham, pp. 275-282. https://doi.org/10.1007/978-3-319-04117-9_25
  • Chen, Y., Li, X., Dick, A., Hill, R., 2014b. Ranking consistency for image matching and object retrieval. Pattern Recognition 47 (3), 1349 - 1360, handwriting Recognition and other PR Applications. https://doi.org/10.1016/j.patcog.2013.09.011
  • Csurka, G., Csurka, G., Dance, C. R., Fan, L., Willamowski, J., Bray, C., 2004. Visual categorization with bags of keypoints. IN WORKSHOP ON STATISTICAL LEARNING IN COMPUTER VISION, ECCV, 1--22.
  • Digabel, H., Lantuéjoul, C., 1978. Iterative algorithms. Actes du Second Symposium Europ'een d'Analyse Quantitative des Microstructures en Sciences des Matériaux, Biologie et Médecine 1 (1), 85-99.
  • Fang, Y., Lei, J., Li, J., Xu, L., Lin, W., Callet, P. L., 2017. Learning visual saliency from human fixations for stereoscopic images. Neurocomputing 266, 284 - 292. https://doi.org/10.1016/j.neucom.2017.05.050
  • Fidalgo, E., Alegre, E., González-Castro, V., Fernández-Robles, L., 2016. Compass radius estimation for improved image classification using Edge-SIFT. Neurocomputing 197, 119-135. https://doi.org/10.1016/j.neucom.2016.02.045
  • Fidalgo, E., Alegre, E., González-Castro, V., Fernández-Robles, L., 2017. Illegal activity categorisation in DarkNet based on image classification using CREIC method. 10th International Conference on Computational Intelligence in Security for Information Systems I (1), 600-609. https://doi.org/10.1007/978-3-319-67180-2_58
  • Fidalgo, E., Alegre, E., González-Castro, V., Fernández-Robles, L., 2018. Boosting image classification through semantic attention filtering strategies. Pattern Recognition Letters. https://doi.org/10.1016/j.patrec.2018.06.033
  • Field, D. J., dec 1987. Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America. A, Optics and image science 4 (12), 2379-94. https://doi.org/10.1364/JOSAA.4.002379
  • Gangwar, A., Fidalgo, E., Alegre, E., González-Castro, V., 2017. Pornography and child sexual abuse detection in image and video: A comparative evaluation. In: 8th International Conference on Imaging for Crime Detection and Prevention (ICDP 2017). pp. 37–42. https://doi.org/10.1049/ic.2017.0046
  • García-Olalla, O., Alegre, E., Fernández-Robles, L., Fidalgo, E., Saikia, S., apr 2018. Textile retrieval based on image content from CDC and webcam cameras in indoor environments. Sensors (Switzerland) 18 (5), 1329. https://doi.org/10.3390/s18051329
  • Gonzalez, R., Woods, R., 2002. Digital image processing. Prentice Hall. https://doi.org/10.1016/0734-189X(90)90171-Q
  • González-Castro, V., Valdés Hernández, M. d. C., Chappell, F. M., Armitage, P. A., Makin, S., Wardlaw, J. M., 2017. Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance. Clinical Science 131 (13), 1465-1481. https://doi.org/10.1042/CS20170051
  • Greenspan, H., Belongie, S., Goodman, R., Perona, P., Rakshit, S., Anderson, C., 1994. Overcomplete steerable pyramid filters and rotation invariance. Computer Vision and Pattern Recognition, 1994. Proceedings CVPR '94., 1994 IEEE Computer Society Conference on, 0-6. https://doi.org/10.1109/CVPR.1994.323833
  • Harel, J., Koch, C., Perona, P., 2007. Graph-Based Visual Saliency.
  • He, Y., Deng, G., Wang, Y., Wei, L., Yang, J., Li, X., Zhang, Y., 2018. Optimization of sift algorithm for fast-image feature extraction in line-scanning ophthalmoscope. Optik 152, 21 - 28. https://doi.org/10.1016/j.ijleo.2017.09.075
  • Hou, X., Harel, J., Koch, C., 2012. Image signature: Highlighting sparse salient regions. IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (1), 194-201. https://doi.org/10.1109/TPAMI.2011.146
  • Itti, L., Koch, C., Niebur, E., 1998. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (11), 1254-1259. https://doi.org/10.1109/34.730558
  • Jian, M.,Wu, L., Jung, C., Fu, Q., Jia, T., 2018. Visual saliency estimation using constraints. Neurocomputing 290, 1 - 11. https://doi.org/10.1016/j.neucom.2018.02.004
  • Kienzle, W., Wichmann, F., Sch¨olkopf, B., Franz, M., 2007. A Nonparametric Approach to Bottom-Up Visual Saliency. Advances in Neural Information Processing Systems 19 19 (December 2006), 689-696.
  • Lahouli, I., Karakasis, E., Haelterman, R., Chtourou, Z., Cubber, G. D., Gasteratos, A., Attia, R., 2018. Hot spot method for pedestrian detection using saliency maps, discrete chebyshev moments and support vector machine. IET Image Processing 12 (7), 1284-1291. https://doi.org/10.1049/iet-ipr.2017.0221
  • Lazebnik, S., Schmid, C., Ponce, J., 2005. A maximum entropy framework for part-based texture and object recognition. Proceedings of the IEEE International Conference on Computer Vision I (1), 832-838. https://doi.org/10.1109/ICCV.2005.10
  • Lowe, D. G., 2004. Distinctive image features from scale invariant keypoints. Int'l Journal of Computer Vision 60, 91-11020042. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  • Mallat, S., mar 2009. Geometrical grouplets. Applied and Computational Harmonic Analysis 26 (2), 161-180. https://doi.org/10.1016/j.acha.2008.03.004
  • Margolin, R., Zelnik-Manor, L., Tal, A., may 2013. Saliency for image manipulation. Visual Computer 29 (5), 381-392. https://doi.org/10.1007/s00371-012-0740-x
  • Murray, N., Vanrell, M., Otazu, X., Parraga, C. A., nov 2013. Low-level spatiochromatic grouping for saliency estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (11), 2810-2816. https://doi.org/10.1109/TPAMI.2013.108
  • Otsu, N., 1979. A threshold selection method from Gray-level. IEEE Transactions on Systems, Man, and Cybernetics SMC-9 (1), 62-66. https://doi.org/10.1109/TSMC.1979.4310076
  • Pinto, N., Doukhan, D., DiCarlo, J. J., Cox, D. D., nov 2009. A high-throughput screening approach to discovering good forms of biologically inspired visual representation. PLoS Computational Biology 5 (11), e1000579. https://doi.org/10.1371/journal.pcbi.1000579
  • Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., Fei-Fei, L., dec 2015. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision 115 (3), 211-252. https://doi.org/10.1007/s11263-015-0816-y
  • Saikia, S., Fidalgo, E., Alegre, E., Fernández-Robles, L., sep 2017. Object Detection for Crime Scene Evidence Analysis Using Deep Learning. In: International Conference on Image Analysis and Processing. Springer, Cham, pp. 14-24. https://doi.org/10.1007/978-3-319-68548-9_2
  • Saikia, S., Fidalgo, E., Alegre, E., Fernández-Robles, L., 2018. Query based object retrieval using neural codes. In: Advances in Intelligent Systems and Computing. Vol. 649. Springer, Cham, pp. 513-523. https://doi.org/10.1007/978-3-319-67180-2_50
  • Sepúlveda, G. V., Torriti, M. T., Calero, M. F., 2017. Sistema de detección de señales de tráfico para la localización de intersecciones viales y frenado anticipado. Revista Iberoamericana de Automática e Informática Industrial 14 (2), 152-162. https://doi.org/10.1016/j.riai.2016.09.010
  • Shen, X., Wu, Y., jun 2012. A unified approach to salient object detection via low rank matrix recovery. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, pp. 853-860. https://doi.org/10.1109/CVPR.2012.6247758
  • Tilke, J., Ehinger, K., Durand, F., Torralba, A., sep 2009. Learning to predict where humans look. In: Proceedings of the IEEE International Conference on Computer Vision. IEEE, pp. 2106-2113. https://doi.org/10.1109/ICCV.2009.5459462
  • Toet, A., Sadaka, N. G., Karam, jun 2009. Frequency-tuned salient region detection. Vision Research 45 (1), II - 169-II - 172. https://doi.org/10.1109/CVPR.2009.5206596
  • Trzcinski, T., Christoudias, M., Lepetit, V., mar 2015. Learning image descriptors with boosting. IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (3), 597-610. https://doi.org/10.1109/TPAMI.2014.2343961
  • van de Weijer, J., Schmid, C., 2006. Coloring Local Feature Extraction. In: Computer Vision - ECCV 2006. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 334-348. https://doi.org/10.1007/11744047_26
  • Vapnik, V. N., 2000. The Nature of Statistical Learning Theory. Springer New York. https://doi.org/10.1007/978-1-4757-3264-1
  • Vedaldi, A., Fulkerson, B., 2010. Vlfeat. Proceedings of the international conference on Multimedia - MM '10 3 (1), 1469. https://doi.org/10.1145/1873951.1874249
  • Vikram, T. N., Tscherepanow, M., Wrede, B., sep 2012. A saliency map based on sampling an image into random rectangular regions of interest. In: Pattern Recognition. Vol. 45. pp. 3114-3124. https://doi.org/10.1016/j.patcog.2012.02.009
  • Yan, Q., Xu, L., Shi, J., Jia, J., 2013. Hierarchical saliency detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. No. 2. pp. 1155-1162. https://doi.org/10.1109/CVPR.2013.153
  • Zhang, L., Gu, Z., Li, H., sep 2013. SDSP: A novel saliency detection method by combining simple priors. In: 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings. pp. 171-175. https://doi.org/10.1109/ICIP.2013.6738036
  • Zhao, Q., Koch, C., jun 2012. Learning visual saliency by combining featuremaps in a nonlinear manner using AdaBoost. Journal of Vision 12 (6), 22. https://doi.org/10.1167/12.6.22
  • Zheng, L., Wang, S., Liu, Z., Tian, Q., jun 2013. Lp-Norm IDF for Large Scale Image Search. Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, 1626-1633. https://doi.org/10.1109/CVPR.2013.213
  • Zheng, L., Wang, S., Zhou, W., Tian, Q., jun 2014. Bayes merging of multiple vocabularies for scalable image retrieval. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, pp. 1963-1970. https://doi.org/10.1109/CVPR.2014.252