Majlesi Journal of Electrical Engineering Combining Hadamard Matrix, Discrete Wavelet Transform and DCT Features based on PCA and KNN for Image Retrieval Volume 7 (2013) Issue 1, March 2013 2024 02 25 Combining Hadamard Matrix, Discrete Wavelet Transform and DCT Features based on PCA and KNN for Image Retrieval EN Hassan Farsi Department of Electronics and Communications Engineering, University of Birjand, Birjand, Iran Sajad Mohamadzadeh Department of Electronics and Communications Eng., University of Birjand, Birjand, Iran Journal Article 2024 02 25 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.