A Comparative Study of Feature Extraction Techniques and Similarity Measures for Image Retrieval

With the growing popularity of using massive amount image database in several applications, it is critical to develop an autonomous and efficient retrieval system to search the relevant images from entire database. The method of obtaining the relevant images from huge image libraries by extracting their content features is known as content-based image retrieval (CBIR). In this paper, a comparative study is performed while acquiring various methods of traditional feature extraction, such as Color moment, Gabor wavelet, Discrete wavelet transform (DWT), Local binary pattern (LBP), Gray level co-occurrence matrix (GLCM), and Histogram of orientation (HOG), to present an efficient and more accurate CBIR system. The experiment is demonstrated on two benchmark datasets, namely Wang (color images) and Medical MNIST (grayscale images), with different visual effects. To retrieve relevant images of a query image, three distinct distance metrics, such as Cosine, City block, and Euclidean, are used to examine the similarity between the query image and the database images. The experiment is evaluated using two performance metrics: precision and recall, to compare the efficacy of various approach. We achieve the best results as average precision of 65.65% and average recall of 6.57 on a scale of 10 using Color moment features via Euclidean distance metric in case of WANG dataset, while 99.89% and 9.99 on a scale of 10 for average precision and average recall using HOG features via City block distance metric in case of Medical MNIST dataset.