Book | Chapter
Ranking-based vocabulary pruning in bag-of-features for image retrieval
pp. 436-445
Abstract
Content-based image retrieval (CBIR) has been applied to a variety of medical applications, e.g., pathology research and clinical decision support, and bag-of-features (BOF) model is one of the most widely used techniques. In this study, we address the problem of vocabulary pruning to reduce the influence from the redundant and noisy visual words. The conditional probability of each word upon the hidden topics extracted using probabilistic Latent Semantic Analysis (pLSA) is firstly calculated. A ranking method is then proposed to compute the significance of the words based on the relationship between the words and topics. Experiments on the publicly available Early Lung Cancer Action Program (ELCAP) database show that the method can reduce the number of words required while improving the retrieval performance. The proposed method is applicable to general image retrieval since it is independent of the problem domain.
Publication details
Published in:
Randall Marcus (2015) Artificial life and computational intelligence: first Australasian conference, acalci 2015, Newcastle, nsw, India, february 5-7, 2015. proceedings. Dordrecht, Springer.
Pages: 436-445
DOI: 10.1007/978-3-319-14803-8_34
Full citation:
Zhang Fan, Song Yang, Cai Weidong, Liu Sidong, Liu Siqi, Dagan Feng David (2015) „Ranking-based vocabulary pruning in bag-of-features for image retrieval“, In: M. Randall (ed.), Artificial life and computational intelligence, Dordrecht, Springer, 436–445.