1
MsC of Electrical Engineering, Semnan University
2
Dep. of Electrical and Computer Engineering, Semnan University
Abstract
With the growing Internet and digital imaging tools, the size of the image database is increasing rapidly. Therefore, there is a strong need for tools and methods to search for images in a large database. Feature extraction is the most basic step in creating an image-retrieval systems. This paper presents a new method for image retrieval systems. After extracting the feature and computing descriptors for each category by the SIFT algorithm, then the appropriate descriptors are identified by the TF-IDF algorithm and used clustering to find candidate descriptors for each category. In the next step, the descriptor coefficients of each category were used with regard to the representatives from the previous stage by the local coding algorithm as the attribute. Finally we used Extreme Learning Machine (ELM) for classification. Experimental results show that the accuracy achieved in proposed method on the Caltech-101 database is about 98.5% and in Flowers data set is about 97.9%.
Jaberi, M., & Yaghmaee, F. (2021). Classification of retrieved images based on SIFT and Locality Constrained Linear Coding. Journal of Machine Vision and Image Processing, 8(2), 73-84.
MLA
Mohsen Jaberi; Farzin Yaghmaee. "Classification of retrieved images based on SIFT and Locality Constrained Linear Coding". Journal of Machine Vision and Image Processing, 8, 2, 2021, 73-84.
HARVARD
Jaberi, M., Yaghmaee, F. (2021). 'Classification of retrieved images based on SIFT and Locality Constrained Linear Coding', Journal of Machine Vision and Image Processing, 8(2), pp. 73-84.
VANCOUVER
Jaberi, M., Yaghmaee, F. Classification of retrieved images based on SIFT and Locality Constrained Linear Coding. Journal of Machine Vision and Image Processing, 2021; 8(2): 73-84.
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