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 o f 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 geogra phical 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.

Multiscale Texture Analysis and Color Coherence Vector Based Feature Descriptor for Multispectral Image Retrieval

“…al (2014) [26]. Local Binary Patterns (LBP), Local Ternary Patterns (LTP), Local Terra Patterns (LTrP) and Local Derivative Patterns (LDP) are most popular texture extraction methods evaluated by [27,28,29]. Color and Texture descriptor based image retrieval system has been developed by using Block Difference Inverse Probability (BDIP) and Block Variation of Local Correlation Coefficients (BVLC) by Chandan Singh, et.al (2016) [30].…”

A Novel Run-length based wavelet features for Screening Thyroid Nodule Malignancy

“…, where i,j are the depth and width indices of the wavelet coefficients matrix Wij. The quantized coefficients q ij W is describe by the equation (15).…”

“…Numerous texture feature extraction methods, depending on wavelet transform, and other statistical descriptors [11][12][13][14], have been used for ultrasound images. Jenitta and Samson [15] utilized Local Vector Pattern (LVP) to separate fine features present in the geographical image and recover the pertinent images from a vast remote sensing image database.…”