Abstract: Image retrieval is a key challenges in many image database application and still an active field in computer vision application. There are many proposed image retrieval systems that retrieve images based on image contents such as colors, texture, shapes and feature descriptor. The main task for image retrieval system is to create a system that capable to retrieve images that are semantically related to user's query from an image database. When user interest to retrieve images that contain a particular objects instead of retrieve similar images which might not related images to his interesting this is called object based on image retrieval. The goal of Objects-based is to retrieve images based on objects that appear in those query images from large database. In this study we use enhanced Speeded UP Robust Features (SURF) algorithm as main step to extract features from interested query objects and then checked and matched result to retrieve related images from image dataset. Speeded UP Robust Features (SURF) is a scale and rotation invariant detector and descriptor feature algorithm and was applied successfully in many Image retrieval systems due its robust against different image transformation. Finally the result written in a report and images saved along with user's query time stamp.
Tawfiq A. Al- asadi and Ahmed J. Obaid, 2016. Object-Based Image Retrieval Using Enhanced SURF. Asian Journal of Information Technology, 15: 2756-2762.