کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
527686 869346 2014 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multiview Hessian discriminative sparse coding for image annotation
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Multiview Hessian discriminative sparse coding for image annotation
چکیده انگلیسی


• mHDSC conducts sparse coding for multiview features.
• mHDSC explores the heterogeneity of diverse features.
• mHDSC boosts the discrimination through the label information.
• Hessian regularization preserves the high-order information of the local geometry.
• Hessian regularization steers the solution varying smoothly along the geodesic.

Sparse coding represents a signal sparsely by using an overcomplete dictionary, and obtains promising performance in practical computer vision applications, especially for signal restoration tasks such as image denoising and image inpainting. In recent years, many discriminative sparse coding algorithms have been developed for classification problems, but they cannot naturally handle visual data represented by multiview features. In addition, existing sparse coding algorithms use graph Laplacian to model the local geometry of the data distribution. It has been identified that Laplacian regularization biases the solution towards a constant function which possibly leads to poor extrapolating power. In this paper, we present multiview Hessian discriminative sparse coding (mHDSC) which seamlessly integrates Hessian regularization with discriminative sparse coding for multiview learning problems. In particular, mHDSC exploits Hessian regularization to steer the solution which varies smoothly along geodesics in the manifold, and treats the label information as an additional view of feature for incorporating the discriminative power for image annotation. We conduct extensive experiments on PASCAL VOC’07 dataset and demonstrate the effectiveness of mHDSC for image annotation.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Vision and Image Understanding - Volume 118, January 2014, Pages 50–60
نویسندگان
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