LSI: Latent semantic inference for natural image segmentation. (November 2016)

• Record Type: Journal Article
• Authors: Dong, Le; Feng, Ning; Zhang, Qianni
• Is Part Of: Pattern recognition. Volume 59(2016:Nov.)
• Page Start: 282
• Page End: 291
• Publication Date: 2016-11
• Subjects: Image Segmentation -- MRF Model -- Label Inference; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.03.005
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: We propose a novel label inference approach for segmenting natural images into perceptually meaningful regions. Each pixel is assigned a serial label indicating its category using a Markov Random Field (MRF) model. To this end, we introduce a framework for latent semantic inference of serial labels, called LSI, by integrating local pixel, global region, and scale information of an natural image into a MRF-inspired model. The key difference from traditional MRF based image segmentation methods is that we infer semantic segments in the label space instead of the pixel space. We first design a serial label formation algorithm named Color and Location Density Clustering (CLDC) to capture the local pixel information. Then we propose a label merging strategy to combine global cues of labels in the Cross-Region potential to grasp the contextual information within an image. In addition, to align with the structure of segmentation, a hierarchical label alignment mechanism is designed to formulate the Cross-Scale potential by utilizing the scale information to catch the hierarchy of image at different scales for final segmentation optimization. We evaluate the performance of the proposed approach on the Berkeley Segmentation Dataset and preferable results are achieved. Abstract : Highlights: We do latent semantic inference for natural image segmentation with Cross-R&S model. We formulate segmentation problem as label inference problem. We design the LSI to capture the context, layout, and scale information of pixels. Cross-Region and Cross-Scale potentials are designed in the LSI framework.