AIR: An Agent for Robust Image Matching and Retrieval
Journal Title: International Journal of Intelligent Systems and Applications in Engineering - Year 2013, Vol 1, Issue 2
Abstract
This paper presents a novel scheme coined AIR (Agent for Image Recognition), acting as an agent, to oversee the image matching and retrieval processes. Firstly, neighboring keypoints within close spatial proximity are examined and used to hypothesize true keypoint matches. While this approach is robust to noise (e.g. a tree) since spatial relation is considered, missing (undetected) keypoints in one image can also be recovered resulting in more keypoint matches. Secondly, the agent is able to recognize instability of projective transformations in certain cases (e.g. non-planar scenes). The geometric approach is substituted with LIS (Longest Increasing Subsequence) approach which does not require any complex geometric transformations. The effectiveness of AIR is substantiated by an image retrieval experiment which demonstrates that it achieves a twofold increase in true matches and higher matching accuracy when compared to RANSAC homography approach.
Authors and Affiliations
Jimmy Addison Lee*| Institute for Infocomm Research, Singapore, Attila Szabó| Institute for Infocomm Research, Singapore, Yiqun Li| Institute for Infocomm Research, Singapore
Keywords
Related Articles
- EP ID EP742
- DOI -
- Views 585
- Downloads 48
Classification of Neurodegenerative Diseases using Machine Learning Methods
In this study, neurodegenerative diseases (Amyotrophic Lateral Sclerosis, Huntington’s disease, and Parkinson’s disease) were diagnosed and classified using force signals. In the classification, five machine learning al...
A simple Mathematical Fuzzy Model of Brain Emotional Learning to Predict Kp Geomagnetic Index
In this paper, we propose fuzzy mathematical model of brain limbic system (LS) which is responsible for emotional stimuli. Here the proposed model is utilized to predict the chaotic activity of the earth’s magnetosphere....
A fuzzy approach for determination of prostate cancer
Goal of this study is a design of a fuzzy expert system, its application aspects in the medicine area and its introduction for calculation of numeric value of prostate cancer risk. For this aim it was used prostate speci...
Predicting Student Success in Courses via Collaborative Filtering
Based on their skills and interests, students’ success in courses may differ greatly. Predicting student success in courses before they take them may be important. For instance, students may choose elective courses that...
Fuzzy Multicriterial Methods for the Selection of IT-Professionals
This paper presents the solution of issues related to selection based on evaluation of demand set forth to IT specialists, to develop appropriate decision support system. In this case problem is reduced to multicriterial...