Unsupervised image segmentation based on analysis of binary partition tree for salient object extraction

This paper proposes an unsupervised image segmentation approach aimed at salient object extraction. Starting from an over-segmentation result of a color image, region merging is performed using a novel dissimilarity measure considering the impact of color difference, area factor and adjacency degree, and a binary partition tree (BPT) is generated to record the whole merging sequence. Then based on a systematic analysis of the evaluated BPT, an appropriate subset of nodes is selected from the BPT to represent a meaningful segmentation result with a small number of segmented regions. Experimental results demonstrate that the proposed approach can obtain a better segmentation performance from the perspective of salient object extraction.     

Robust common visual pattern discovery using graph matching

Discovering common visual patterns (CVPs) between two images is a difficult and time-consuming task, due to the photometric and geometric transformations. The state-of-the-art methods for CVPs discovery are either computationally expensive or have complicated constraints. In this paper, we formulate CVPs discovery as a graph matching problem, depending on pairwise geometric compatibility between feature correspondences. To efficiently find all CVPs, we propose a novel framework which consists of three components: Preliminary Initialization Optimization (PIO), Guided Expansion (GE) and Post Agglomerative Combination (PAC). PIO gets the initial CVPs and reduces the search space of CVPs discovery, based on the internal homogeneity of CVPs. Then, GE anchors on the initializations and gradually explores them, to find more and more correct correspondences. Finally, to reduce false and miss detection, PAC refines the discovery result in an agglomerative way. Experiments and applications conducted on benchmark datasets demonstrate the effectiveness and efficiency of our method.