A Hybrid Geometric Spatial Image Representation for scene classification.
The recent development in the technology has increased the complexity of image contents and demand for image classification becomes more imperative. Digital images play a vital role in many applied domains such as remote sensing, scene analysis, medical care, textile industry and crime investigation...
Main Authors: | , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2018-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC6135402?pdf=render |
id |
doaj-9318447960264f149fd29d7bcdffcf9e |
---|---|
record_format |
Article |
spelling |
doaj-9318447960264f149fd29d7bcdffcf9e2020-11-24T22:18:52ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01139e020333910.1371/journal.pone.0203339A Hybrid Geometric Spatial Image Representation for scene classification.Nouman AliBushra ZafarFaisal RiazSaadat Hanif DarNaeem Iqbal RatyalKhalid Bashir BajwaMuhammad Kashif IqbalMuhammad SajidThe recent development in the technology has increased the complexity of image contents and demand for image classification becomes more imperative. Digital images play a vital role in many applied domains such as remote sensing, scene analysis, medical care, textile industry and crime investigation. Feature extraction and image representation is considered as an important step in scene analysis as it affects the image classification performance. Automatic classification of images is an open research problem for image analysis and pattern recognition applications. The Bag-of-Features (BoF) model is commonly used to solve image classification, object recognition and other computer vision-based problems. In BoF model, the final feature vector representation of an image contains no information about the co-occurrence of features in the 2D image space. This is considered as a limitation, as the spatial arrangement among visual words in image space contains the information that is beneficial for image representation and learning of classification model. To deal with this, researchers have proposed different image representations. Among these, the division of image-space into different geometric sub-regions for the extraction of histogram for BoF model is considered as a notable contribution for the extraction of spatial clues. Keeping this in view, we aim to explore a Hybrid Geometric Spatial Image Representation (HGSIR) that is based on the combination of histograms computed over the rectangular, triangular and circular regions of the image. Five standard image datasets are used to evaluate the performance of the proposed research. The quantitative analysis demonstrates that the proposed research outperforms the state-of-art research in terms of classification accuracy.http://europepmc.org/articles/PMC6135402?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nouman Ali Bushra Zafar Faisal Riaz Saadat Hanif Dar Naeem Iqbal Ratyal Khalid Bashir Bajwa Muhammad Kashif Iqbal Muhammad Sajid |
spellingShingle |
Nouman Ali Bushra Zafar Faisal Riaz Saadat Hanif Dar Naeem Iqbal Ratyal Khalid Bashir Bajwa Muhammad Kashif Iqbal Muhammad Sajid A Hybrid Geometric Spatial Image Representation for scene classification. PLoS ONE |
author_facet |
Nouman Ali Bushra Zafar Faisal Riaz Saadat Hanif Dar Naeem Iqbal Ratyal Khalid Bashir Bajwa Muhammad Kashif Iqbal Muhammad Sajid |
author_sort |
Nouman Ali |
title |
A Hybrid Geometric Spatial Image Representation for scene classification. |
title_short |
A Hybrid Geometric Spatial Image Representation for scene classification. |
title_full |
A Hybrid Geometric Spatial Image Representation for scene classification. |
title_fullStr |
A Hybrid Geometric Spatial Image Representation for scene classification. |
title_full_unstemmed |
A Hybrid Geometric Spatial Image Representation for scene classification. |
title_sort |
hybrid geometric spatial image representation for scene classification. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2018-01-01 |
description |
The recent development in the technology has increased the complexity of image contents and demand for image classification becomes more imperative. Digital images play a vital role in many applied domains such as remote sensing, scene analysis, medical care, textile industry and crime investigation. Feature extraction and image representation is considered as an important step in scene analysis as it affects the image classification performance. Automatic classification of images is an open research problem for image analysis and pattern recognition applications. The Bag-of-Features (BoF) model is commonly used to solve image classification, object recognition and other computer vision-based problems. In BoF model, the final feature vector representation of an image contains no information about the co-occurrence of features in the 2D image space. This is considered as a limitation, as the spatial arrangement among visual words in image space contains the information that is beneficial for image representation and learning of classification model. To deal with this, researchers have proposed different image representations. Among these, the division of image-space into different geometric sub-regions for the extraction of histogram for BoF model is considered as a notable contribution for the extraction of spatial clues. Keeping this in view, we aim to explore a Hybrid Geometric Spatial Image Representation (HGSIR) that is based on the combination of histograms computed over the rectangular, triangular and circular regions of the image. Five standard image datasets are used to evaluate the performance of the proposed research. The quantitative analysis demonstrates that the proposed research outperforms the state-of-art research in terms of classification accuracy. |
url |
http://europepmc.org/articles/PMC6135402?pdf=render |
work_keys_str_mv |
AT noumanali ahybridgeometricspatialimagerepresentationforsceneclassification AT bushrazafar ahybridgeometricspatialimagerepresentationforsceneclassification AT faisalriaz ahybridgeometricspatialimagerepresentationforsceneclassification AT saadathanifdar ahybridgeometricspatialimagerepresentationforsceneclassification AT naeemiqbalratyal ahybridgeometricspatialimagerepresentationforsceneclassification AT khalidbashirbajwa ahybridgeometricspatialimagerepresentationforsceneclassification AT muhammadkashifiqbal ahybridgeometricspatialimagerepresentationforsceneclassification AT muhammadsajid ahybridgeometricspatialimagerepresentationforsceneclassification AT noumanali hybridgeometricspatialimagerepresentationforsceneclassification AT bushrazafar hybridgeometricspatialimagerepresentationforsceneclassification AT faisalriaz hybridgeometricspatialimagerepresentationforsceneclassification AT saadathanifdar hybridgeometricspatialimagerepresentationforsceneclassification AT naeemiqbalratyal hybridgeometricspatialimagerepresentationforsceneclassification AT khalidbashirbajwa hybridgeometricspatialimagerepresentationforsceneclassification AT muhammadkashifiqbal hybridgeometricspatialimagerepresentationforsceneclassification AT muhammadsajid hybridgeometricspatialimagerepresentationforsceneclassification |
_version_ |
1725781063716831232 |