Effective hashing for large-scale multimedia search
Proceedings of the 2013 Sigmod/PODS Ph.D. symposium on PhD symposium
A MapReduce-based distributed SVM ensemble for scalable image classification and annotation
Computers & Mathematics with Applications
Content based image retrieval via a transductive model
Journal of Intelligent Information Systems
Hi-index | 0.00 |
Biased discriminant analysis (BDA) is one of the most promising relevance feedback (RF) approaches to deal with the feedback sample imbalance problem for content-based image retrieval (CBIR). However, the singular problem of the positive within-class scatter and the Gaussian distribution assumption for positive samples are two main obstacles impeding the performance of BDA RF for CBIR. To avoid both of these intrinsic problems in BDA, in this paper, we propose a novel algorithm called generalized BDA (GBDA) for CBIR. The GBDA algorithm avoids the singular problem by adopting the differential scatter discriminant criterion (DSDC) and handles the Gaussian distribution assumption by redesigning the between-class scatter with a nearest neighbor approach. To alleviate the overfitting problem, GBDA integrates the locality preserving principle; therefore, a smooth and locally consistent transform can also be learned. Extensive experiments show that GBDA can substantially outperform the original BDA, its variations, and related support-vector-machine-based RF algorithms.