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Analysis of SURF and SIFT representations to recognize food objects

The social media services such as Facebook, Instagram and Twitter has attracted millions of food photos to be uploaded every day since its inception. Automatic analysis on food images are beneficial from health, cultural and marketing aspects. Hence, recognizing food objects using image processing a...

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Main Authors: Razali, Mohd Norhisham, Manshor, Noridayu, Abdul Halin, Alfian, Mustapha, Norwati, Yaakob, Razali
Format: Article
Language:English
Published: Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka 2017
Online Access:http://psasir.upm.edu.my/id/eprint/60860/1/Analysis%20of%20SURF%20and%20SIFT%20representations%20to%20recognize%20food%20objects.pdf
http://psasir.upm.edu.my/id/eprint/60860/
http://journal.utem.edu.my/index.php/jtec/article/view/2774
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spelling my.upm.eprints.608602019-04-23T07:20:03Z http://psasir.upm.edu.my/id/eprint/60860/ Analysis of SURF and SIFT representations to recognize food objects Razali, Mohd Norhisham Manshor, Noridayu Abdul Halin, Alfian Mustapha, Norwati Yaakob, Razali The social media services such as Facebook, Instagram and Twitter has attracted millions of food photos to be uploaded every day since its inception. Automatic analysis on food images are beneficial from health, cultural and marketing aspects. Hence, recognizing food objects using image processing and machine learning techniques has become emerging research topic. However, to represent the key features of foods has become a hassle from the immaturity of current feature representation techniques in handling the complex appearances, high deformation and large variation of foods. To employ many kinds of feature types are also infeasible as it inquire much pre-processing and computational resources for segmentation, feature representation and classification. Motivated from these drawbacks, we proposed the integration on two kinds of local feature namely Speeded-Up Robust Feature (SURF) and Scale Invariant Feature Transform (SIFT) to represent the features large variation food objects. Local invariant features have shown to be successful in describing object appearances for image classification tasks. Such features are robust towards occlusion and clutter and are also invariant against scale and orientation changes. This makes them suitable for classification tasks with little inter-class similarity and large intra-class difference. The Bag of Features (BOF) approach is employed to enhance the discriminative ability of the local features. Experimental results demonstrate impressive overall recognition at 82.38% classification accuracy from the local feature integration based on the challenging UEC-Food100 dataset. Then, we provide depth analysis on SURF and SIFT implementation to highlight the problems towards recognizing foods that need to be rectified in the future research. Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka 2017 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/60860/1/Analysis%20of%20SURF%20and%20SIFT%20representations%20to%20recognize%20food%20objects.pdf Razali, Mohd Norhisham and Manshor, Noridayu and Abdul Halin, Alfian and Mustapha, Norwati and Yaakob, Razali (2017) Analysis of SURF and SIFT representations to recognize food objects. Journal of Telecommunication, Electronic and Computer Engineering, 9. 81 - 88. ISSN 2180-1843; ESSN: 2289-8131 http://journal.utem.edu.my/index.php/jtec/article/view/2774
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description The social media services such as Facebook, Instagram and Twitter has attracted millions of food photos to be uploaded every day since its inception. Automatic analysis on food images are beneficial from health, cultural and marketing aspects. Hence, recognizing food objects using image processing and machine learning techniques has become emerging research topic. However, to represent the key features of foods has become a hassle from the immaturity of current feature representation techniques in handling the complex appearances, high deformation and large variation of foods. To employ many kinds of feature types are also infeasible as it inquire much pre-processing and computational resources for segmentation, feature representation and classification. Motivated from these drawbacks, we proposed the integration on two kinds of local feature namely Speeded-Up Robust Feature (SURF) and Scale Invariant Feature Transform (SIFT) to represent the features large variation food objects. Local invariant features have shown to be successful in describing object appearances for image classification tasks. Such features are robust towards occlusion and clutter and are also invariant against scale and orientation changes. This makes them suitable for classification tasks with little inter-class similarity and large intra-class difference. The Bag of Features (BOF) approach is employed to enhance the discriminative ability of the local features. Experimental results demonstrate impressive overall recognition at 82.38% classification accuracy from the local feature integration based on the challenging UEC-Food100 dataset. Then, we provide depth analysis on SURF and SIFT implementation to highlight the problems towards recognizing foods that need to be rectified in the future research.
format Article
author Razali, Mohd Norhisham
Manshor, Noridayu
Abdul Halin, Alfian
Mustapha, Norwati
Yaakob, Razali
spellingShingle Razali, Mohd Norhisham
Manshor, Noridayu
Abdul Halin, Alfian
Mustapha, Norwati
Yaakob, Razali
Analysis of SURF and SIFT representations to recognize food objects
author_facet Razali, Mohd Norhisham
Manshor, Noridayu
Abdul Halin, Alfian
Mustapha, Norwati
Yaakob, Razali
author_sort Razali, Mohd Norhisham
title Analysis of SURF and SIFT representations to recognize food objects
title_short Analysis of SURF and SIFT representations to recognize food objects
title_full Analysis of SURF and SIFT representations to recognize food objects
title_fullStr Analysis of SURF and SIFT representations to recognize food objects
title_full_unstemmed Analysis of SURF and SIFT representations to recognize food objects
title_sort analysis of surf and sift representations to recognize food objects
publisher Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka
publishDate 2017
url http://psasir.upm.edu.my/id/eprint/60860/1/Analysis%20of%20SURF%20and%20SIFT%20representations%20to%20recognize%20food%20objects.pdf
http://psasir.upm.edu.my/id/eprint/60860/
http://journal.utem.edu.my/index.php/jtec/article/view/2774
_version_ 1643837450963910656
score 13.211869

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