# Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification Source: [Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:57:48.173Z Authors: Yong Luo, Dacheng Tao, Chang Xu, Chao Xu, Hong Liu, Yonggang Wen Published: 2019-04-08T09:57:18.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()