Mohammed Reda Guedira, A. E. Qadi, Mohammed Rziza Lrit, M. Hassouni
{"title":"A novel method for image categorization based on histogram oriented gradient and support vector machine","authors":"Mohammed Reda Guedira, A. E. Qadi, Mohammed Rziza Lrit, M. Hassouni","doi":"10.1109/EITECH.2017.8255229","DOIUrl":null,"url":null,"abstract":"In this paper, we introduce a new method for categorisation natural image based on different techniques. Concerning the color and texture, we made a pre-treatment to convert the database images on to the gray-scale and the Haar wavelet transformation. For this, we use the Oriented Gradient Histogram (HOG) for each sub-band to extract these image features. We have used a proper classification based on the support vector machine (SVM) to recognize these images. The result part and experience applied on a Corel database that is known in natural images shows a better performance of the proposed system based on accuracy and speed compared to other CBIR methods.","PeriodicalId":447139,"journal":{"name":"2017 International Conference on Electrical and Information Technologies (ICEIT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Electrical and Information Technologies (ICEIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EITECH.2017.8255229","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
In this paper, we introduce a new method for categorisation natural image based on different techniques. Concerning the color and texture, we made a pre-treatment to convert the database images on to the gray-scale and the Haar wavelet transformation. For this, we use the Oriented Gradient Histogram (HOG) for each sub-band to extract these image features. We have used a proper classification based on the support vector machine (SVM) to recognize these images. The result part and experience applied on a Corel database that is known in natural images shows a better performance of the proposed system based on accuracy and speed compared to other CBIR methods.