Robust discriminative extreme learning machine for relevance feedback in image retrieval

Relevance feedback (RF) has long been an important approach for multi-media retrieval because of the semantic gap in image content, where SVM based methods are widely applied to RF of content-based image retrieval. However, RF based on SVM still has some limitations: (1) the high dimension of image...

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Bibliographic Details
Published in:Multidimensional systems and signal processing 2017-07, Vol.28 (3), p.1071-1089
Main Authors: Liu, Shenglan, Feng, Lin, Liu, Yang, Wu, Jun, Sun, MuXin, Wang, Wei
Format: Article
Language:English
Subjects:
Artificial Intelligence
Benchmarks
Circuits and Systems
Criteria
Datasets
Discrimination
Electrical Engineering
Engineering
Feedback
Image management
Image retrieval
Labels
Learning
Matrices (mathematics)
Multimedia
Neural networks
Robustness
Scattering
Signal,Image and Speech Processing
State of the art
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Summary:Relevance feedback (RF) has long been an important approach for multi-media retrieval because of the semantic gap in image content, where SVM based methods are widely applied to RF of content-based image retrieval. However, RF based on SVM still has some limitations: (1) the high dimension of image features always make the RF time-consuming; (2) the model of SVM is not discriminative, because labels of image features are not sufficiently exploited. To solve above problems, we proposed robust discriminative extreme learning machine (RDELM) in this paper. RDELM involved both robust within-class and between-class scatter matrices to enhance the discrimination capacity of ELM for RF. Furthermore, an angle criterion dimensionality reduction method is utilized to extract the discriminative information for RDELM. Experimental results on four benchmark datasets (Corel-1K, Corel-5K, Corel-10K and MSRC) illustrate that our proposed RF method in this paper achieves better performance than several state-of-the-art methods.
ISSN:0923-6082
1573-0824
DOI:10.1007/s11045-016-0386-3