An Efficient Image Retrieval System By Optimizing KNN
Keywords:
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 colour signature with the shape and colour texture features. Where the features are extracted from the given Query Image in the similar fashion. After that the number of cluster formed in a dataset. Cluster formation based on finding the Euclidean distance between each pair in a dataset. Consequently, a novel image retrieval using k nearest neighbor(KNN) classifier is achieved between the features of the Query Image and the features of the cluster images. 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.Abstract
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 colour signature with the shape and colour texture features. Where the features are extracted from the given Query Image in the similar fashion. After that the number of cluster formed in a dataset. Cluster formation based on finding the Euclidean distance between each pair in a dataset. Consequently, a novel image retrieval using k nearest neighbor(KNN) classifier is achieved between the features of the Query Image and the features of the cluster images. 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.
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