Object Segmentation Using Multiple Neural Networks for Commercial Offers Visual Search

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.
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Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.209-218, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_24〉
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I. Gallo, A. Nodari, M. Vanetti. Object Segmentation Using Multiple Neural Networks for Commercial Offers Visual Search. Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.209-218, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_24〉. 〈hal-01571327〉

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