title: A Probabilistic Associative Model for Segmenting Weakly-Supervised Images.
abstract: Weakly-supervised image segmentation is an important yet challenging task in image processing and pattern recognition fields. It is defined as: in the training stage, semantic labels are only at the image-level, without regard to their specific object/scene location within the image. Given a test image, the goal is to predict the semantics of every pixel/superpixel. In this paper, we propose a new weakly-supervised image segmentation model, focusing on learning the semantic associations between superpixel sets (graphlets in this work). In particular, we first extract graphlets from each image, where a graphlet is a small-sized graph measures the potential of multiple spatially neighboring superpixels (i.e., the probability of these superpixels sharing a common semantic label, such as the "sky" or the "sea"). To compare dierent-sized graphlets and to incorporate image-level labels, a manifold embedding algorithm is designed to transform all graphlets into equal-length feature vectors. Finally, we present a hierarchical Bayesian network (BN) to capture the semantic associations between post-embedding graphlets, based on which the semantics of each superpixel is inferred accordingly. Experimental results demonstrate that: 1) our approach performs competitively compared with the state-of-the-art approaches on three public data sets, and 2) considerable performance enhancement is achieved when using our approach on segmentation-based photo cropping and image categorization.
authors.forename: Luming
authors.initials: L
authors.lastname: Zhang
authors.forename: Yi
authors.initials: Y
authors.lastname: Yang
authors.forename: Yue
authors.initials: Y
authors.lastname: Gao
authors.forename: Yi
authors.initials: Y
authors.lastname: Yu
authors.forename: Changbo
authors.initials: C
authors.lastname: Wang
authors.forename: Xuelong
authors.initials: X
authors.lastname: Li