Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification
This addresses multi-label image classification for computer vision applications, presenting an incremental improvement by combining existing techniques of vector-valued functions and manifold regularization with multi-view features.
The paper tackles multi-label image classification by integrating multiple visual features and label relationships, introducing multi-view vector-valued manifold regularization (MV³MR) to exploit feature complementarity and shared geometry. Experiments on PASCAL VOC'07 and MIR Flickr datasets validated its effectiveness, though specific numerical gains are not detailed in the abstract.
In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e.g. pedestrian, bicycle and tree) and is properly characterized by multiple visual features (e.g. color, texture and shape). Currently available tools ignore either the label relationship or the view complementary. Motivated by the success of the vector-valued function that constructs matrix-valued kernels to explore the multi-label structure in the output space, we introduce multi-view vector-valued manifold regularization (MV$\mathbf{^3}$MR) to integrate multiple features. MV$\mathbf{^3}$MR exploits the complementary property of different features and discovers the intrinsic local geometry of the compact support shared by different features under the theme of manifold regularization. We conducted extensive experiments on two challenging, but popular datasets, PASCAL VOC' 07 (VOC) and MIR Flickr (MIR), and validated the effectiveness of the proposed MV$\mathbf{^3}$MR for image classification.