borders); (b) shows cutouts using these scribbles. A naïve interactive co-segmentation setup would force a user to examine all cutouts for mistakes, and then iteratively scribble on the worst segmentation to obtain better results. Cutouts needing correction are shown with red borders. (c) shows the region prompted for more scribbles by iCoseg, thus avoiding exhaustive examination of all cutouts by users. This paper presents an algorithm for Interactive Cosegmentation of a foreground object from a group of related images. While previous approaches focus on unsupervised co- segmentation, we use successful ideas from the interactive object- cutout literature. We develop an algorithm that allows users to decide what foreground is, and then guide the output of the co- segmentation algorithm towards it via scribbles. Interestingly, keeping a user in the loop leads to simpler and highly parallelizable energy functions, allowing us to work with significantly more images per group. However, unlike the interactive single image counterpart, a

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We propose an approach to identify and segment objects from scenes that a person (or robot) encounters in Activities of Daily Living (ADL). Images collected in those cluttered scenes contain multiple objects. Each image provides only a partial, possibly very different view of each object. An object instance discovery program must be able to link pieces of visual information from multiple images and extract the consistent patterns. Most papers on unsupervised discovery of object models are concerned with object categories. In contrast, this paper aims at identifying and extracting regions corresponding to specific object instances, e.g., two different laptops in the laptop category. By focusing on specific instances, we enforce explicit constraints on geometric consistency (such as scale, orientation), and appearance consistency (such as color, texture and shape). Using multiple segmentations as the basic building block, our program processes a noisy “soup ” of segments and extracts object models as groups of mutually consistent segments. Our approach was tested on three different types of image sets: two from indoor ADL environments and one from Flickr.com. The results demonstrate robustness of our program to severe clutter, occlusion, changes of viewpoint and interference from irrelevant images. Our approach achieves significant improvement over with two existing methods. 1.

Abstract. We present an interactive system to create 3D models of objects of interest in their natural cluttered environments. A typical setting for 3D modeling of an object of interest involves capturing images from multiple views in a multi-camera studio with a mono-color screen or structured lighting. This is a tedious process and cannot be applied to a variety of objects. Moreover, general scene reconstruction algorithms fail to focus on the object of interest to the user. In this paper, we use successful ideas from the object cut-out literature, and develop an interactive-cosegmentation-based algorithm that uses scribbles from the user indicating foreground (object to be modeled) and background (clutter) to extract silhouettes of the object of interest from multiple views. Using these silhouettes, and the camera parameters obtained from structure-from-motion, in conjunction with a shape-from-silhouette algorithm we generate a texture-mapped 3D model of the object of interest. 1

We study the problem of segmenting specific white matter structures of interest from Diffusion Tensor (DT-MR) images of the human brain. This is an important requirement in many Neuroimaging studies: for instance, to evaluate whether a brain structure exhibits group level differences as a function of disease in a set of images. Typically, interactive expert guided segmentation has been the method of choice for such applications, but this is tedious for large datasets common today. To address this problem, we endow an image segmentation algorithm with “advice ” encoding some global characteristics of the region(s) we want to extract. This is accomplished by constructing (using expert-segmented images) an epitome of a specific region – as a histogram over a bag of ‘words ’ (e.g., suitable feature descriptors). Now, given such a representation, the problem reduces to segmenting a new brain image with additional constraints that enforce consistency between the segmented foreground and the pre-specified histogram over features. We present combinatorial approximation algorithms to incorporate such domain specific constraints for Markov Random Field (MRF) segmentation. Making use of recent results on image co-segmentation, we derive effective solution strategies for our problem. We provide an analysis of solution quality, and present promising experimental evidence showing that many structures of interest in Neuroscience can be extracted reliably from 3-D brain image volumes using our algorithm. 1

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In this paper, we address a challenging image segmentation problem called multiple foreground cosegmentation (MFC), which concerns a realistic scenario in general Webuser photo sets where a finite number of K foregrounds of interest repeatedly occur over the entire photo set, but only an unknown subset of them is presented in each image. This contrasts the classical cosegmentation problem dealt with by most existing algorithms, which assume a much simpler but less realistic setting where the same set of foregrounds recurs in every image. We propose a novel optimization method for MFC, which makes no assumption on foreground configurations and does not suffer from the aforementioned limitation, while still leverages all the benefits of having co-occurring or (partially) recurring contents across images. Our method builds on an iterative scheme that alternates between a foreground modeling module and a region assignment module, both highly efficient and scalable. In particular, our approach is flexible enough to integrate any advanced region classifiers for foreground modeling, and our region assignment employs a combinatorial auction framework that enjoys several intuitively good properties such as optimality guarantee and linear complexity. We show the superior performance of our method in both segmentation quality and scalability in comparison with other state-of-the-art techniques on a newly introduced FlickrMFC dataset and the standard ImageNet dataset. 1.