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Object Segmentation Using Multiple Neural Networks for Commercial Offers Visual Search

  • Conference paper

Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 363)

Abstract

We describe a web application that takes advantage of new computer vision techniques to allow the user to make searches based on visual similarity of color and texture related to the object of interest. We use a supervised neural network strategy to segment different classes of objects. A strength of this solution is the high speed in generalization of the trained neural networks, in order to obtain an object segmentation in real time. Information about the segmented object, such as color and texture, are extracted and indexed as text descriptions. Our case study is the online commercial offers domain where each offer is composed by text and images. Many successful experiments were done on real datasets in the fashion field.

Keywords

  • visual object segmentation
  • visual search
  • multiple neural networks

References

  1. Wang, J.Z., Li, J., Wiederhold, G.: Simplicity: Semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 23, 947–963 (2001)

    CrossRef  Google Scholar 

  2. Liu, Y., Zhang, D., Lu, G., Ma, W.Y.: A survey of content-based image retrieval with high-level semantics. Pattern Recognition 40, 262–282 (2007)

    MATH  CrossRef  Google Scholar 

  3. Skopal, T.: Where are you heading, metric access methods?: a provocative survey. In: Proc. of SISAP 2010, pp. 13–21. ACM, New York (2010)

    Google Scholar 

  4. Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20, 273–297 (1995)

    MATH  Google Scholar 

  5. Gallo, I., Nodari, A.: Learning object detection using multiple neural netwoks. In: VISAP 2011. INSTICC Press (2011)

    Google Scholar 

  6. Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: The rprop algorithm. In: IEEE Conf. on Neural Networks, pp. 586–591 (1993)

    Google Scholar 

  7. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. CVPR, pp. 886–893 (2005)

    Google Scholar 

  8. Nodari, A., Gallo, I., Cavallaro, A.: Real time color image indexing using a textual approach. Submitted to ICIP 2011 (2011)

    Google Scholar 

  9. Everingham, M., Van Gool, L., et al.: The pascal visual object classes (voc) challenge. International Journal of Computer Vision 88, 303–338 (2010)

    CrossRef  Google Scholar 

  10. Pedoia, V., Colli, V., et al.: fMRI analysis software tools: an evaluation framework. In: SPIE Medical Imaging 2011 (2011)

    Google Scholar 

  11. Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33, 159–174 (1977)

    MathSciNet  MATH  CrossRef  Google Scholar 

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© 2011 International Federation for Information Processing

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Gallo, I., Nodari, A., Vanetti, M. (2011). Object Segmentation Using Multiple Neural Networks for Commercial Offers Visual Search. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN AIAI 2011 2011. IFIP Advances in Information and Communication Technology, vol 363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23957-1_24

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  • DOI: https://doi.org/10.1007/978-3-642-23957-1_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23956-4

  • Online ISBN: 978-3-642-23957-1

  • eBook Packages: Computer ScienceComputer Science (R0)