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Object Segmentation Using Multiple Neural Networks for Commercial Offers Visual Search

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


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.


  • visual object segmentation
  • visual search
  • multiple neural networks


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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.

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  • 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)