Experimental analysis of SIFT and SURF features for multi-object image retrieval

Abstract

Content-based image retrieval (CBIR) is the most challenging task of retrieving similar images for large databases. In this work, we present experimental analysis based on scale invariant feature transform (SIFT) and speeded up robust features (SURF) as local features for multi-object image retrieval. The experiments are conducted on database consisting of group of images obtained by aggregating two objects, three objects, four objects and five objects together from the Columbia Object Image Library (COIL-100) image dataset of ten categories. For the query image, features are extracted using SIFT or SURF. These features of query image are compared with corresponding features stored in the database for similarity retrieval. The precision, recall and FMeasure values are computed for each group and results are tabulated. The experimental results show that the accuracy of retrieval from SURF is better than SIFT method.