IJE TRANSACTIONS B: Applications Vol. 26, No. 11 (November 2013) 1281-1288   

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R. AhilaPriyadharshini and S. Arivazhagan
( Received: August 25, 2012 – Accepted in Revised Form: May 16, 2013 )

Abstract    The proposed method is to recognize objects based on application of Local Steering Kernels (LSK) as Descriptors to the image patches. In order to represent the local properties of the images, patch is to be extracted where the variations occur in an image. To find the interest point, Wavelet based Salient Point detector is used. Local Steering Kernel is then applied to the resultant pixels, in order to obtain the most promising features. The features extracted will be over complete so in order to reduce dimensionality, Principal Component Analysis (PCA) is applied. Further, the sparse histogram is taken over the PCA output. The classifier used here is Support Vector Machine (SVM) Classifier. Bench mark database used here is UIUC car database and the results obtained are satisfactory. The results obtained using LSK kernel is compared by varying parameters such as patch size, number of salient points/patches, smoothing parameter and scaling parameter.


Keywords    Object Recognition, Salient Point Detector, Patch extraction, Local Steering Kernel, Principal Component Analysis.


چکیده    روش ارائه شده براي تشخيص اشیاء بر اساس استفاده از Local Steering Kernels (LSK) به عنوان توصیفگر تکه های تصویر مي باشد. به منظور نشان دادن خواص يك نقطه از تصاویر که در آن تغییرات رخ می دهد ، پچ مورد استفاده قرار مي گيرد. برای پیدا کردن نقطه مورد نظر، مبتنی بر آشکارساز موجي نقطه برجسته استفاده می شود. سپس، از روش (LSK) به منظور دست يابي به پیکسل مطلوب استفاده مي شود. ویژگی های استخراج شده بيش از حد كامل بوده؛ بنابراین، به منظور کاهش ابعاد، تجزیه و تحلیل اجزای اصلی، روش (PCA) اعمال می شود. علاوه بر این، نمایش طرز انتشار وفواصل وارتفاع سلول ها بیش از PCA خروجی، از هم پراکنده است. طبقه بندی مورد استفاده در اینجا ماشین بردار (SVM) طبقه بندی پشتیبانی است. علامت مشخصه پایگاه داده با استفاده از بانک اطلاعاتی خودرو UIUC است و نتایج به دست آمده رضایت بخش است. نتایج به دست آمده با استفاده از هسته LSK از لحاظ پارامترهای مختلف مانند اندازه پچ، تعدادی از نقاط برجسته به نسبت پچ ها، پارامترهاي وضوح و پارامتر هاي اندازه قابل مقايسه مي باشد.



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