Visual-Semantic Scene Understanding by Sharing Labels in a Context Network
This work addresses scene understanding for computer vision applications, but it appears incremental as it builds on existing models like Pachinko Allocation and Latent Dirichlet Allocation.
The paper tackles the problem of naming objects in complex natural scenes with varying appearance and subtle name differences by proposing the Visual Semantic Integration Model (VSIM), which shares object labels between visual and semantic contexts. It reports that VSIM surpasses state-of-the-art methods on the SUN09 dataset.
We consider the problem of naming objects in complex, natural scenes containing widely varying object appearance and subtly different names. Informed by cognitive research, we propose an approach based on sharing context based object hypotheses between visual and lexical spaces. To this end, we present the Visual Semantic Integration Model (VSIM) that represents object labels as entities shared between semantic and visual contexts and infers a new image by updating labels through context switching. At the core of VSIM is a semantic Pachinko Allocation Model and a visual nearest neighbor Latent Dirichlet Allocation Model. For inference, we derive an iterative Data Augmentation algorithm that pools the label probabilities and maximizes the joint label posterior of an image. Our model surpasses the performance of state-of-art methods in several visual tasks on the challenging SUN09 dataset.