Label to region by bi-layer sparsity priors

In this work, we investigate how to automatically reassign the manually annotated labels at the image-level to those contextually derived semantic regions. First, we propose a bi-layer sparse coding formulation for uncovering how an image or semantic region can be robustly reconstructed from the over-segmented image patches of an image set. We then harness it for the automatic label to region assignment of the entire image set. The solution to bi-layer sparse coding is achieved by convex ℓ1 -norm minimization. The underlying philosophy of bi-layer sparse coding is that an image or semantic region can be sparsely reconstructed via the atomic image patches belonging to the images with common labels, while the robustness in label propagation requires that these selected atomic patches come from very few images. Each layer of sparse coding produces the image label assignment to those selected atomic patches and merged candidate regions based on the shared image labels. The results from ...

• Type: Conference
• Year: 2009
• Where: MM
• Authors: Xiaobai Liu, Bin Cheng, Shuicheng Yan, Jinhui Tang, Tat-Seng Chua, Hai Jin
• Keywords: Bi-layer Sparse Coding, Image, MM 2009, Multimedia, Semantic Region