کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
530513 869772 2015 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
IODA: An input/output deep architecture for image labeling
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
IODA: An input/output deep architecture for image labeling
چکیده انگلیسی


• IODA is a neural network architecture that directly links a whole image to a whole label decision map.
• It is based on a pretraining of the outputs, i.e. of the label dependencies.
• We outperform the state-of-the-art approach on a real-world medical imaging problem.
• An open source implementation is provided.

In this paper, we propose a deep neural network (DNN) architecture called Input Output Deep Architecture (IODA) for solving the problem of image labeling. IODA directly links a whole image to a whole label map, assigning a label to each pixel using a single neural network forward step. Instead of designing a handcrafted a priori model on labels (such as an atlas in the medical domain), we propose to automatically learn the dependencies between labels. The originality of IODA is to transpose DNN input pre-training trick to the output space, in order to learn a high level representation of labels. It allows a fast image labeling inside a fully neural network framework, without the need of any preprocessing such as feature designing or output coding.In this paper, IODA is applied on both a toy texture problem and a real-world medical image dataset, showing promising results. We provide an open source implementation of IODA.1,2

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 48, Issue 9, September 2015, Pages 2847–2858
نویسندگان
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