Abstract
This paper proposes an Image Retrieval method using the Bag of Word model combining with Hierarchical K-Means++ algorithm to store and arrange databases, called BoW-HKM++ approach. A Hierarchical tree is proposed to improve the processing speed in retrieval data. In this research: (1) we first build a bag of word model based on SIFT feature extraction on images; (2) then show its improvement by using inverted index technical for whole feature databases; (3) the Hierarchical K-Means++ algorithm is also proposed in the bag of word model to optimize retrieval system. Experiment results were evaluated with k-fold cross-validation method on 2 datasets of James Z. Wang Research Group. Finally, the obtained results will be compared with other method and discussed to prove the effectiveness of proposed framework in Image Retrieval problems.
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Kieu, M., Lai, K.D., Tran, T.D., Le, T.H. (2016). A Fusion of Bag of Word Model and Hierarchical K-Means++ in Image Retrieval. In: Huynh, VN., Inuiguchi, M., Le, B., Le, B., Denoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2016. Lecture Notes in Computer Science(), vol 9978. Springer, Cham. https://doi.org/10.1007/978-3-319-49046-5_34
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DOI: https://doi.org/10.1007/978-3-319-49046-5_34
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