Combining Hadamard Matrix, Discrete Wavelet Transform and DCT Features based on PCA and KNN for Image Retrieval

Farsi, Hassan; Mohamadzadeh, Sajad

Document Type : Review Article

Authors

Department of Electronics and Communications Eng., University of Birjand, Birjand, Iran

Abstract

Image retrieval is one of the most applicable image processing techniques which have been used extensively. Feature extraction is one of the most important procedures used for interpretation and indexing images in content-based image retrieval (CBIR) systems. Reducing dimension of feature vector is one of challenges in CBIR systems. There are many proposed methods to overcome these challenges. However, the rate of image retrieval and speed of retrieval is still an interesting field of researches. In this paper we propose a new method based on combination of Hadamard matrix, discrete wavelet transform (HDWT2) and discrete cosine transform (DCT) and we used principal component analysis (PCA) to reduce dimension of feature vector and K-nearest neighbor (KNN) for similarity measurement. The precision at percent recall and ANR are considered as metrics to evaluate and compare different methods. Obtaining results show that the proposed method provides better performance in comparison with other methods.

Keywords

References

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Majlesi Journal of Electrical Engineering

Combining Hadamard Matrix, Discrete Wavelet Transform and DCT Features based on PCA and KNN for Image Retrieval

References

References

Volume 7, Issue 1 - Serial Number 1
March 2013

Combining Hadamard Matrix, Discrete Wavelet Transform and DCT Features based on PCA and KNN for Image Retrieval

References

References

Volume 7, Issue 1 - Serial Number 1March 2013

Volume 7, Issue 1 - Serial Number 1
March 2013