TY - EJOUR
AU - Farsi, Hassan
AU - Mohamadzadeh, Sajad
PY - 2024
DA - February
TI - Combining Hadamard Matrix, Discrete Wavelet Transform and DCT Features based on PCA and KNN for Image Retrieval
T2 - Majlesi Journal of Electrical Engineering
VL - 7
L1 - https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/combining-hadamard-matrix-discrete-wavelet-transform-and-dct-features-based-on-pca-and-knn-for-image-retrieval/
N2 - 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. 
IS - 1
PB - OICC Press
KW - Clustering Error. Dataset, Content-based image retrieval (CBIR), Hadamard matrix and discrete wave let transform (HDWT2), discrete cosine transform (DCT) 
EN -