LAUSR

Abstract This paper presents an efficient image retrieval approach based on enhanced Bag of Words (BOW). By keeping eyes on low classification efficiency and accuracy of existing image retrieval system based on traditional BOW, a new classification method combined features of scale invariant feature transform (SIFT) and GIST principle (CGSF) is proposed. This method use SIFT algorithm to extract the local feature vector and GIST algorithm to extract the global feature vector for images. The feature fusion scheme is applied to combine local with global features through weight which can enhance the classification accuracy with efficiency. At the end, VLFeat linear support vector machine (SVM) classifier is used to classify the visual dictionary and Osirix medical computed tomography (CT) image dataset is selected for experimental verification. Three experiments are carried out based on factors that contribute to performance and accuracy (dictionary size and λ co-efficient). The experimental results prove that our proposed technique is much robust and accurate than traditional algorithms in image retrieval. Furthermore, our method is matched with literature in classification accuracy and the outcomes indicate the noticeable benefits in medical, internet of things and many other domains.