کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
383393 660820 2012 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Texture descriptor based on partially self-avoiding deterministic walker on networks
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Texture descriptor based on partially self-avoiding deterministic walker on networks
چکیده انگلیسی

Texture image analysis is an important field of investigation that has attracted the attention from computer vision community in the last decades. In this paper, a novel approach for texture image analysis is proposed by using a combination of graph theory and partially self-avoiding deterministic walks. From the image, we build a regular graph where each vertex represents a pixel and it is connected to neighboring pixels (pixels whose spatial distance is less than a given radius). Transformations on the regular graph are applied to emphasize different image features. To characterize the transformed graphs, partially self-avoiding deterministic walks are performed to compose the feature vector. Experimental results on three databases indicate that the proposed method significantly improves correct classification rate compared to the state-of-the-art, e.g. from 89.37% (original tourist walk) to 94.32% on the Brodatz database, from 84.86% (Gabor filter) to 85.07% on the Vistex database and from 92.60% (original tourist walk) to 98.00% on the plant leaves database. In view of these results, it is expected that this method could provide good results in other applications such as texture synthesis and texture segmentation.


► A multi-agent approach for texture modeling and recognition is proposed.
► It builds a regular graph from texture image and then transformations are applied to enhance different properties of the texture.
► A self-avoiding deterministic walk is applied for each node in order to obtain a feature vector.
► Experiments are performed on two widely used texture databases and a real case of species of plants.
► Experimental results show the effectiveness of the proposed method compared to state-of-the-art methods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 39, Issue 15, 1 November 2012, Pages 11818–11829
نویسندگان
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