An Efficient Content Based Image Retrieval System By Optimizing SOH
S Agalya · Zenodo (CERN European Organization for Nuclear Research) · 2018
This work presents a Content-Based Image Retrieval (CBIR) system embedded with a clustering technique to retrieve images similar to query image. In this work, extensive robust and important features were extracted from the images database and then stored in the feature repository. This feature set is composed of color signature with the shape and color texture features. After that number of cluster formed in dataset. Cluster formation based on find Euclidean distance between each pairs in dataset QI image feature extraction is based on salient orientation histogram. Consequently, a novel image retrieval using k nearest neighbour (KNN) classifier is achieved between the features of the QI and the features of the cluster images. This method is entirely different from the existing histograms, most of the existing histogram techniques merely count the number or frequency of pixels. However, the unique characteristic of SOH is that they count the perceptually uniform color difference between two points under different backgrounds with regard to colors and edge orientations. Our proposed CBIR system is assessed by inquiring number of images (from the test dataset) and the efficiency of the system is evaluated by calculating precision-recall value for the results. The results were superior to other state-of-the-art CBIR systems in regard to precision