Abstract
As for the huge gap between the low-level image features and high-level semantics, content-based image retrieval still could not receive a satisfying result by now. Since the special request of the relevance feedback, making full use of the rare number of labeled data and numerous unlabeled data is an ideal way. Because ELM has excellent classification accuracy and processing time, and high accuracy and fast speed are the key factors to evaluate the relevance feedback performances. In this paper, we proposed an Extreme learning Machine with Gaussian kernel Based Relevance Feedback scheme for image retrieval, to overcome the above limitations, our method uses three component classifiers to form a strong learner by learning different features extracted from the hand-marking data, then we use it to label the image database automatically. From the experiments we can see the use of the ELM with kernel have high classification accuracy, the processing time get largely decreased at the same time. Thus, it improves the efficiency of entire relevance feedback system. The experiments results show that the proposed algorithm is significantly effective.
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Acknowledgement
This research is partially sponsored by Natural Science Foundation of China (Nos. 61003105, 61175115 and 61370113), the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (CIT&TCD201304035), Jing-Hua Talents Project of Beijing University of Technology (2014-JH-L06), and Ri-Xin Talents Project of Beijing University of Technology (2014-RX-L06), and the International Communication Ability Development Plan for Young Teachers of Beijing University of Technology (No. 2014-16).
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Duan, L., Dong, S., Cui, S., Ma, W. (2016). Extreme Learning Machine with Gaussian Kernel Based Relevance Feedback Scheme for Image Retrieval. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-28397-5_31
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DOI: https://doi.org/10.1007/978-3-319-28397-5_31
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