Content Based Geographic Image Retrieval using Local Vector Pattern

ABSTRACT Large image archives formed by satellite remote sensing missions are getting an increasing valuable source of information in Geographic Information Systems (GIS). The need for retrieving a required image from a huge image database is increasing significantly for the purpose of analyzing resources in GIS. Content Based Geographic Image Retrieval (CBGIR) in the image processing field is the best solution to meet the requirement. In this work, we used Local Vector Pattern (LVP) to extract fine features present in the geographical image and retrieve the applicable images from a large remote sensing image database. The primary idea of our method is generating micro patterns of LVP by the vectors of each pixel that are constructed by calculating the values between the centre pixels and its neighbourhood pixels with various distances of different directions. Then the proposed method was designed for concatenating these vector patterns to produce more unique features of geographical images and comparing the results with Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Tetra Pattern (LTrP). Ultimately, the extensive analysis carried out on different geographical image collections proved that the proposed method achieves the improved classification accuracy and better retrieving results.

Image Retrieval Based on Local Mesh Vector Co-occurrence Pattern for Medical Diagnosis from MRI Brain Images.

In modern health-care, for evidence-based diagnosis, there is a requirement for an efficient image retrieval approach to retrieve the cases of interest that have similar characteristics from the large image databases. This paper presents a feature extraction approach that aims at extracting texture features present in the medical images using Local Pattern Descriptor (LPD) and Gray-level Co-occurrence Matrix (GLCM). As a main contribution, a novel local pattern named Local Mesh Vector Co-occurrence Pattern (LMVCoP) has been proposed by concatenating the Local Mesh Co-occurrence Pattern (LMCoP) and the Local Vector Co-occurrence Pattern (LVCoP). The fusion of GLCM with the Local Mesh Pattern (LMeP) and the Local Vector Pattern (LVP) produces LMCoP and LVCoP respectively. The LMVCoP method has been investigated on the Open Access Series of Imaging Studies (OASIS): a Magnetic Resonance Imaging (MRI) brain image database. LMVCoP descriptor achieves 87.57% of ARP and 53.21% of ARR which are higher than the existing methods of LTCoP, PVEP, LBDP, LMeP and LVP. The LMVCoP method enhances the retrieval results of LMeP/LVP from 81.36%/83.52% to 87.57% in terms of ARP on OASIS MRI brain database.