Local Variation as a Statistical Hypothesis Test

The goal of image oversegmentation is to divide an image into several pieces, each of which should ideally be part of an object. One of the simplest and yet most effective oversegmentation algorithms is known as local variation (LV) (Felzenszwalb and Huttenlocher 2004). In this work, we study this algorithm and show that algorithms similar to LV can be devised by applying different statistical models and decisions, thus providing further theoretical justification and a well-founded explanation for the unexpected high performance of the LV approach. Some of these algorithms are based on statistics of natural images and on a hypothesis testing decision; we denote these algorithms probabilistic local variation (pLV). The best pLV algorithm, which relies on censored estimation, presents state-of-the-art results while keeping the same computational complexity of the LV algorithm

Segmentation Propagation in ImageNet

Abstract. ImageNet is a large-scale hierarchical database of object classes. We propose to automatically populate it with pixelwise segmentations, by leveraging existing manual annotations in the form of class labels and bounding-boxes. The key idea is to recursively exploit images segmented so far to guide the segmentation of new images. At each stage this propagation process expands into the images which are easiest to segment at that point in time, e.g. by moving to the semantically most related classes to those segmented so far. The propagation of segmentation occurs both (a) at the image level, by transferring existing segmentations to estimate the probability of a pixel to be foreground, and (b) at the class level, by jointly segmenting images of the same class and by importing the appearance models of classes that are already segmented. Through an experiment on 577 classes and 500k images we show that our technique (i) annotates a wide range of classes with accurate segmentations; (ii) effectively exploits the hierarchical structure of ImageNet; (iii) scales efficiently; (iv) outperforms a baseline GrabCut [1] initialized on the image center, as well as our recent segmentation transfer technique [2] on which this paper is based. Moreover, our method also delivers state-of-the-art results on the recent iCoseg dataset for co-segmentation.

Contextual hypergraph modeling for salient object detection

Salient object detection aims to locate objects that capture human attention within images. Previous approaches often pose this as a problem of image contrast analysis. In this work, we model an image as a hypergraph that utilizes a set of hyperedges to capture the contextual properties of image pixels or regions. As a result, the problem of salient object detection becomes one of finding salient vertices and hyperedges in the hypergraph. The main advantage of hypergraph modeling is that it takes into account each pixel’s (or region’s) affinity with its neighborhood as well as its separation from image background. Furthermore, we propose an alternative approach based on centerversus- surround contextual contrast analysis, which performs salient object detection by optimizing a cost-sensitive support vector machine (SVM) objective function. Experimental results on four challenging datasets demonstrate the effectiveness of the proposed approaches against the stateof- the-art approaches to salient object detection.Xi Li, Yao Li, Chunhua Shen, Anthony Dick, Anton van den Henge

Ensembles of segmentation algorithms for figure-ground segmentation.

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Extracting Foreground Masks towards Object Recognition

Effective segmentation prior to recognition has been shown to improve recognition performance. However, most segmentation algorithms adopt methods which are not explicitly linked to the goal of object recognition. Here we solve a related but slightly different problem in order to assist object recognition more directly- the extraction of a foreground mask, which identifies the locations of objects in the image. We propose a novel foreground/background segmentation algorithm that attempts to segment the interesting objects from the rest of the image, while maximizing an objective function which is tightly related to object recognition. We do this in a manner which requires no classspecific knowledge of object categories, using a probabilistic formulation which is derived from manually segmented images. The model includes a geometric prior and an appearance prior, whose parameters are learnt on the fly from images that are similar to the query image. We use graphcut based energy minimization to enforce spatial coherence on the model’s output. The method is tested on the challenging VOC09 and VOC10 segmentation datasets, achieving excellent results in providing a foreground mask. We also provide comparisons to the recent segmentation method of [7]. 1