Robust Similar Image Retrieval Based on Extracted Object Features

Abstract

In this paper, a robust similar image retrieval method using extracted object features is proposed. A local texture feature as well as global contour and color features are extracted from an object image to generate unified robust feature vectors. To show the effectiveness of our method, experimental noisy image retrieval was executed using standard object image databases. The recall rate, precision rate and F-measure obtained by cross-validation were calculated to evaluate the performance of object image retrieval. High-performance image retrieval was achieved compared with the conventional methods without using combined robust features of extracted objects.