کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
530910 869798 2014 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multi-local model image set matching based on domain description
ترجمه فارسی عنوان
تطبیق تصویر مدل چند لایه بر اساس توصیف دامنه
کلمات کلیدی
مدل چند محلی، توصیف دامنه، تطبیق مجموعه تصویر ناپایدار، شباهت را تنظیم کنید خوشه بندی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی


• We propose a novel multi-local mode image set matching algorithm.
• It is the first attempt to introduce the SVDD to image set matching.
• We solve the set matching as an optimization problem via domain description.
• We formulate the image set matching as the distances between pair-wise domains.
• Experimental results demonstrate the superior performance of our approach.

Image set matching attracted increasing attention in the field of pattern recognition. Recently, there are a number of effective image set-based matching methods under controlled environment. However in the more complex environment, like multi-view and illumination changed, it is still a challenging problem to develop unsupervised image set matching method to handle multi-local model data. To solve this problem, in this paper, we present a novel multi-local model image set matching method based on data description techniques. First, every image set is divided into multi-local models, and each local model corresponds to a data domain, that is, we innovatively train a support vector data domain to describe each local model by means of the excellent data description ability of support vector data domain, hence each image set can be expressed by a plurality of support vector data domain. Second, a new similarity measure based on domain–domain distance is proposed, and then the distance between two image sets is converted to integrate the distance between pair-wise domains. Finally, the proposed method is evaluated on both set-based face recognition and object classification tasks. Extensive experimental results show that the proposed method outperforms other state of the art set-based matching methods in three public video databases.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 47, Issue 2, February 2014, Pages 694–704
نویسندگان
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