Abstract
Nowadays, with the advent of digital imagery, the volume of digital images has been growing rapidly in different fields; so there is an increasing requirement to effective image retrieval system. Hence, we need a more efficient and effective image searching technology. In this paper, we introduce a new scheme to image annotation in two stage. First semi-supervised k-means clustering with Mahalanobis similarity measure has been used. Second, a novel hybrid relevance feedback algorithm, AHRFC is proposed to narrow the gap between low-level image feature and high-level semantic and improve the accuracy of image annotation. The AHRFC algorithm is compound of three stages: (1) The images that the user knows irrelevant to cluster, are conducted to correct cluster by a long-term RF; (2) Regarding the images that the user knows relevant to cluster, we try to estimate feature weight of the clusters to provide a multiple similarity measure using a re-weighting RF; (3) To approach the exact place of the cluster centers, a cluster center movement (CCM) RF is used. Experimental results on the Corel database and satellite database taken from the Tehran city show the effectiveness of proposed methods in improving the retrieval performance.
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Javani, M., Eftekhari Moghadam, A.M. (2012). An Image Annotation Technique Based on a Hybrid Relevance Feedback Scheme. In: Lukose, D., Ahmad, A.R., Suliman, A. (eds) Knowledge Technology. KTW 2011. Communications in Computer and Information Science, vol 295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32826-8_20
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DOI: https://doi.org/10.1007/978-3-642-32826-8_20
Publisher Name: Springer, Berlin, Heidelberg
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