Given an untextured 3D car models dataset, are we able to learn a robust make and model classifier which will be applied on real color images? One solution consists in finding a common representation between synthetic edge images and real color images. To address this issue, we introduce novel edge-color invariant features for 2D/3D car fine-grained classification. These features are learned simultaneously on real color and edge images of cars using a deep architecture. Using these accurate features, we propose to learn on edges synthetic images a fine-grained classifier which will be afterwards applied to real color images. The 3D car models dataset allows to automatically generate multi-views synthetic images using non-photorealistic edge rendering. Experimentally, we show efficiency of our learned edge-color invariant features for make and model recognition.


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    Title :

    Deep edge-color invariant features for 2D/3D car fine-grained classification


    Contributors:


    Publication date :

    2017-06-01


    Size :

    2166739 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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