IR-HF-WED: IMAGE RETRIEVAL USING HYBRID FEATURE EXTRACTION WITH WEIGHTED EUCLIDEAN DISTANCE

Abstract

Attributable to the fast development of image acquisition and storage modernization, image retrieval plays a key role in different applications such as Medical Imaging, Visual Data Mining etc. Due to high storage complexity Image Retrieval needs complex algorithms to retrieve images in efficient and fast manner. Image Retrieval technique using Hybrid Feature Extraction with Weighted Euclidean Distance (IR-HF-WED) is proposed to reduce the time complexity and increase the accuracy. Multiple features are extracted using Color Co-occurrence Matrix (CCM), Histogram of oriented Gradient (HoG), Compound Local Binary Pattern (CLBP) and Difference Between Pixels of Scanned Patterns (DBPSP). The proposed algorithm is tested and analyzed by comparing with Absolute Distance (AD), Euclidean Distance (ED), Cross Correlation (X) distance matching techniques and it is found that IR-HF-WED outperforms with respect to Precision and Recall compared to the [1, 2]