ارائه روشی جدید از نگاشت توسعه‌یافته الگوی دودویی محلی جهت طبقه‌بندی تصاویر بافتی
محورهای موضوعی : مهندسی برق و کامپیوتر
محمدحسین شکور
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فرشاد فرشاد تاجری‌پور
2
1 - دانشگاه آزاد اسلامي، واحد شيراز
2 - دانشگاه شیراز
کلید واژه: طبقه‌بندی بافت استخراج ویژگی الگوهای دودویی محلی الگوهای محلی همگن و ناهمگن,
چکیده مقاله :
طبقه‌بندی بافت از جمله شاخه‌های مهم پردازش تصویر است و مهم‌ترین نکته در طبقه‌بندی بافت‌ها، استخراج ویژگی‌های تصویر بافتی است. یکی از مهم‌ترین و ساده‌ترین روش‌ها، روش مبتنی بر الگوی دودویی محلی است که به دلیل سادگی در پیاده‌سازی و استخراج ویژگی‌های مناسب با دقت طبقه‌بندی بالا، مورد توجه قرار گرفته است. در اغلب روش‌های الگوی دودویی محلی بیشتر به الگوهای محلی همگن توجه شده و همه اطلاعات قسمت‌های ناهمگن تصویر صرفاً به عنوان یک ویژگی استخراج می‌شود. در این مقاله، یک شکل جدید از نگاشت الگوهای دودویی محلی ارائه شده که از اطلاعات الگوهای ناهمگن به شکل مناسب استفاده می‌کند. یعنی بر خلاف اغلب روش‌های قبلی، در اینجا از الگوهای محلی ناهمگن ویژگی‌های بیشتری استخراج می‌شود و در نتیجه دقت طبقه‌بندی بالاتر می‌رود. ضمن این که کلیه نکات مثبت روش‌های موجود مانند غیر حساس بودن به چرخش و تغییرات روشنایی را دارد. روش ارائه‌شده با استخراج ویژگی‌های بیشتر از الگوهای ناهمگن به دقت بالاتری از طبقه‌بندی نسبت به روش‌های مشهور و مهم دست یافته است. پیاده‌سازی روش ارائه‌شده روی پایگاه بافتی Outex این بهبود را نشان می‌دهد.
Texture classification is one of the important branches of image processing. The main point of texture classification is feature extraction. Local Binary Pattern (LBP) is one of the important methods that are used for texture feature extraction. This method is widely used because it has simple implementation and extracts high discriminative features from textures. Most of previous LBP methods used uniform patterns and only one feature is extracted from non-uniform patterns. In this paper, by extending non-uniform patterns a new mapping technique is proposed that extracts more discriminative features from non-uniform patterns. So in spite of almost all of the previous LBP methods, the proposed method extracts more discriminative features from non-uniform patterns and increases the classification accuracy of textures. The proposed method has all of the positive points of previous LBP variants. It is a rotation invariant and illumination invariant method and increase the classification accuracy. The implementation of proposed mapping on Outex dataset shows that proposed method can improve the accuracy of classifications significantly.
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