Discriminative extreme learning machine to content-based image retrieval with relevance feedback
Xiaodong Huang, Liang Sun, Huihui Guo, Shenglan Liu · 2016
To narrow down the semantic gap and increase the retrieval efficiency in image retrieval, relevance feedback (RF) has long been an important approach, where the active support vector machine (SVM) based RFs are widely applied to content-based image retrieval (CBIR). However, the performance of these methods are often poor because the low speed of SVM algorithm in high dimension data. Meanwhile, the model of SVM is not discriminative, because the labels of the image features are insufficient exploited. To overcome the problems, we propose discriminative extreme learning machine (DELM) in this paper. Both within-class and between-class scatter matrices are involved in DELM to enhance the discrimination capacity of ELM for RF. The experimental results on two benchmark datasets (Corel-1K and Corel-10K) illustrate that our proposed method of this paper achieves a better performance than the state-of-the-art methods.