Cross-media retrieval based on linear discriminant analysis

Existing cross-media retrieval approaches usually project low-level features from different modalities of data into a common subspace, in which the similarity of multi-modal data can be measured directly. However, most of the previous subspace learning methods ignore the discriminative property of multi-modal data which may lead to suboptimal cross-media retrieval performance. To address this problem, we propose a novel approach to cross-media retrieval framework based on Linear Discriminant Analysis (LDA), which integrates the correlation between textual features and visual features to learn a pair of projection matrices so that we can project the low-level heterogeneous features into a shared feature space by the transformation matrices. Thus the discriminative characteristic of textual modality is transferred to the corresponding visual features via the correlation analysis process. Experiments on three benchmark datasets show the effectiveness of our approach.