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Analysis Of Distance Metrics In Content Based Image Retrieval Using Statistical Quantized Histogram Texture Features In The DCT Domain

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Malik, Fazal (Author)
مؤلفين آخرين: Baharudin, Baharum (Co-Author)
المجلد/العدد: مج25, ع2
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2013
الصفحات: 207 - 218
DOI: 10.33948/0584-025-002-009
ISSN: 1319-1578
رقم MD: 972965
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
كلمات المؤلف المفتاحية:
Content Based Image Retrieval (CBIR) | Statistical Texture Features | Distance Metrics | Quantized Histogram | DCT
رابط المحتوى:
صورة الغلاف QR قانون
حفظ في:
LEADER 02409nam a22002417a 4500
001 1715877
024 |3 10.33948/0584-025-002-009 
041 |a eng 
044 |b السعودية 
100 |9 524538  |a Malik, Fazal  |e Author 
245 |a Analysis Of Distance Metrics In Content Based Image Retrieval Using Statistical Quantized Histogram Texture Features In The DCT Domain 
260 |b جامعة الملك سعود  |c 2013 
300 |a 207 - 218 
336 |a بحوث ومقالات  |b Article 
520 |b The effective content-based image retrieval (CBIR) needs efficient extraction of low level features like color, texture and shapes for indexing and fast query image matching with indexed images for the retrieval of similar images. Features are extracted from images in pixel and compressed domains. However, now most of the existing images are in compressed formats like JPEG using DCT (discrete cosine transformation). In this paper we study the issues of efficient extraction of features and the effective matching of images in the compressed domain. In our method the quantized histogram statistical texture features are extracted from the DCT blocks of the image using the significant energy of the DC and the first three AC coefficients of the blocks. For the effective matching of the image with images, various distance metrics are used to measure similarities using texture features. The analysis of the effective CBIR is performed on the basis of various distance metrics in different number of quantization bins. The proposed method is tested by using Corel image database and the experimental results show that our method has robust image retrieval for various distance metrics with different histogram quantification in a compressed domain. 
653 |a علوم الحاسوب  |a الصور الرقمية  |a استرجاع الصور 
692 |b Content Based Image Retrieval (CBIR)  |b Statistical Texture Features  |b Distance Metrics  |b Quantized Histogram  |b DCT 
700 |9 524539  |a Baharudin, Baharum  |e Co-Author 
773 |c 009  |e Journal of King Saud University (Computer and Information Sciences)  |f Maǧalaẗ ǧamʼaẗ al-malīk Saud : ùlm al-ḥasib wa al-maʼlumat  |l 002  |m مج25, ع2  |o 0584  |s مجلة جامعة الملك سعود - علوم الحاسب والمعلومات  |v 025  |x 1319-1578 
856 |u 0584-025-002-009.pdf 
930 |d y  |p y 
995 |a science 
999 |c 972965  |d 972965 

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