کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
411768 679589 2015 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Saliency detection using hierarchical manifold learning
ترجمه فارسی عنوان
تشخیص معنویت با استفاده از یادگیری چندجملهای سلسله مراتبی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Saliency detection is critical to many applications in computer vision by eliminating redundant backgrounds. The saliency detection approaches can be divided into two categories, i.e., top-down and bottom-up. Among them, bottom-up models have attracted more attention due to their simple mechanisms. However, many existing bottom-up models are not robust to crowded backgrounds because of missing salient regions within feedforward frameworks which is often not effective for complex scenes. We tackle these problems by modifying and extending a bottom-up saliency detection model through three phases, (1) constructing a hierarchical sequence of images from the perspective of entropy, (2) estimated mid-level cues are used as feedback information, (3) subsequently generating saliency maps by global context and local uniqueness in a graph-based framework. We also compare the proposed bottom-up model with state-of-the-art approaches on two benchmark datasets to evaluate its saliency detection performance. The experimental results demonstrate that the proposed bottom-up saliency detection approach is not only robust to both cluttered and clean scenes, but also able to obtain objects with different scales.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 168, 30 November 2015, Pages 538–549
نویسندگان
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