Average Analysis Method in Selecting Haralick’s Texture Features on Color Co-occurrence Matrix for Texture Based Image Retrieval

Mamat, Abd Rasid; Awang, Mohd Khalid; Rawi, Norkhairani Abdul; Awang, Mohd Isha; Kadir, Mohd Fadzil Abdul

doi:10.14257/ijmue.2016.11.2.10

Cited by 1 publication

(2 citation statements)

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“…This phase involved two processes namely data acquisition and filtering the images. Data acquisition is obtained from an image from the Wang database (Yue et al, 2011;Mamat et al, 2016a). This database contains 1000 images and is divided into 10 categories and is known as African People, Beach, Building, Buses, Dinosaurs, Elephants, Flowers Horses, Mountains, and Food.…”

Section: Step 1: Pre-processingmentioning

confidence: 99%

“…This is because it is easy to extract rather than shape and texture. In addition, the color feature is relatively robust to background complication and independent of image size and orientation and this has attracted many researchers to use it in their research (Mamat et al, 2016a;Singh and Hemachandran, 2012;Hossain and Islam, 2017;Mamat et al, 2015). Taking advantage of these, color features were used in this study.…”

Section: Step 3: Colour Features Extractionmentioning

confidence: 99%

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Region-based image retrieval using region of interest (ROI) according to incremental frame and clustering color image

al.¹

2022

Int. j. adv. appl. sci.

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Content-based image retrieval involves the extraction of global feature images for their retrieval performance in large image databases. Extraction of global features image cause problem of the semantic gap between the high-level meaning and low-level visual features images. In this study RBIR, Region of Interest Based (ROI) Image Retrieval Using Incremental Frame of Color Image was proposed. It combines several methods, including filtering process, image partitioning using clustering and incremental frame formation, complementation law of theory set to generate ROI, NROI, or ER of the region. The concept of weighting as well as a significant query is also incorporated as a query strategy. Extensive experiments were also conducted on the Wang database and the color model selected was the CIE lab. Experimental results show the proposed method is efficient in image retrieval. The performance of the proposed method shows a better average IPR value of 3.51% compared to RGB and 22.92% with the HSV color model. Meanwhile, it also performs better by 36%, 5%, and 24% compared to methods CH (8,2,2), CH (8,3,3), and CH (16,4,4).

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Section: Step 1: Pre-processingmentioning

confidence: 99%

Section: Step 3: Colour Features Extractionmentioning

confidence: 99%