• Vol 7, No 4 (2016)
  • Electrical, Electronics, and Computer Engineering

Query Region Determination based on Region Importance Index and Relative Position for Region-based Image Retrieval

Pasnur , Agus Zainal Arifin, Anny Yuniarti

Corresponding email: pasnur@akba.ac.id


Published at : 29 Apr 2016
IJtech : IJtech Vol 7, No 4 (2016)
DOI : https://doi.org/10.14716/ijtech.v7i4.1546

Cite this article as:

Pasnur., Arifin, A.Z., & Yuniarti, A. 2016. Query Region Determination based on Region Importance Index and Relative Position for Region-based Image Retrieval. International Journal of Technology. Volume 7(4), pp.654-662

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Pasnur Sekolah Tinggi Manajemen Informatika dan Ilmu Komputer AKBA, Kampus STMIK AKBA Tamalanrea, Makassar 90245, Indonesia
Agus Zainal Arifin Department of Informatics Engineering, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya 60111, Indonesia
Anny Yuniarti Department of Informatics Engineering, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya 60111, Indonesia
Email to Corresponding Author

Abstract
image

An efficient Region-Based Image Retrieval (RBIR) system must consider query region determination techniques and target regions in the retrieval process. A query region is a region that must contain a Region of Interest (ROI) or saliency region. A query region determination can be specified manually or automatically. However, manual determination is considered less efficient and tedious for users. The selected query region must determine specific target regions in the image collection to reduce the retrieval time. This study proposes a strategy of query region determination based on the Region Importance Index (RII) value and relative position of the Saliency Region Overlapping Block (SROB) to produce a more efficient RBIR. The entire region is formed by using the mean shift segmentation method. The RII value is calculated based on a percentage of the region area and region distance to the center of the image. Whereas the target regions are determined by considering the relative position of SROB, the performance of the proposed method is tested on a CorelDB dataset. Experimental results show that the proposed method can reduce the Average of Retrieval Time to 0.054 seconds with a 5x5 block size configuration.

Local binary pattern, Region-based image retrieval, Region importance index, Relative position, Region code, Saliency region

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