Image Retrieval Using Discriminant Embedding and LS-SVM
Ziqiang Wang, Xia Sun · 2009
To efficiently deal with the curse of dimensionality in the content-based image retrieval (CBIR) system, a novel image retrieval algorithm is proposed by combination of local discriminant embedding (LDE) and least square SVM (LS-SVM) in this paper. LDE aims to achieve good discriminating performance by integrating the local geometrical structure and class relations between image data. LS-SVM classifier is used to classify the retrieved image into relevant or irrelevant image based on extracted low-level visual features. Experimental results on real-world image collection demonstrate that the proposed algorithm performs much better than other related image retrieval algorithms.