Authors:
Afra’a Ahmad Alyosef
and
Andreas Nürnberger
Affiliation:
Department of Technical and Business Information Systems, Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany
Keyword(s):
Near-duplicate Image Retrieval, Fuzzy Histogram, HSV Color Histogram, SIFT Keypoints.
Abstract:
Near-duplicate image retrieval is still a challenging task, especially due to issues with matching quality and performance. Most existing approaches use high dimensional vectors based on local features such as SIFT keypoints to represent images. The extraction and matching of these vectors to detect near-duplicates are time and memory consuming. Global features such as color histograms can strongly reduce the dimensionality of image vectors and significantly accelerate the matching process. On the other hand, they strongly decrease the quality of the retrieval process. In this work, we propose a hybrid approach to improve the quality of retrieval and reduce the computation time by applying a robust filtering process using global features optimized for recall followed by a ranking process optimized for precision. For efficient filtering we propose a fuzzy partition hue saturation (HS) histogram to retrieve a subset of near-duplicate candidate images. After that, we re-rank the top ret
rieved results by extracting the SIFT features. In order to evaluate the performance and quality of this hybrid approach, we provide results of a comparative performance analysis using the original SIFT-128D, the HS color histogram, the fuzzy HS model (F-HS), the proposed fuzzy partition HS model (FP-HS) and the combination of the proposed fuzzy partition HS histogram with the SIFT features using large scale image benchmark databases. The results of experiments show that applying the fuzzy partition HS histogram and re-rank the top results (only 6%) of the retrieved images) using the SIFT algorithm significantly outperforms the use of the individual state of art methods with respect to computing efficiently and effectively.
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