Interactive image segmentation algorithms rely on the user to provide annotations as the guidance. When the task of interactive segmentation is performed on a small touchscreen device, the requirement of providing precise annotations could be cumbersome to the user. We design an efficient seed proposal method that actively proposes annotation seeds for the user to label. The user only needs to check which ones of the query seeds are inside the region of interest (ROI). We enforce the sparsity and diversity criteria on the selection of the query seeds. At each round of interaction the user is only presented with a small number of informative query seeds that are far apart from each other. As a result, we are able to derive a user friendly interaction mechanism for annotation on small touchscreen devices. The user merely has to swipe through on the ROI-relevant query seeds, which should be easy since those gestures are commonly used on a touchscreen. The performance of our algorithm is evaluated on six publicly available datasets. The evaluation results show that our algorithm achieves high segmentation accuracy, with short response time and less user feedback.
Another line is the indirect interactive image segmentation @cite_17 @cite_41 @cite_3 @cite_13 @cite_5 , in which the algorithms usually recommend several uncertain regions to the user, and then the segmentation algorithms adopt the user-selected regions for updating the segmentation results. Batra @cite_17 propose a co-segmentation algorithm that provides the suggestion about where the user should draw scribbles next. Based on the active learning method, Fathi @cite_41 present an incremental self-training video segmentation method to ask the user to provide annotations for gradually labeling the frames. For scene reconstruction, Kowdle @cite_3 also employ an active learning algorithm to query the user's scribbles about the uncertain regions. To segment a large 3D dataset, Straehl @cite_5 provide various uncertainty measurements to suggest the user some candidate locations, and then segment the dataset using the watershed cut according to the user-selected locations. Rupprecht @cite_13 model the segmentation uncertainty as a probability distribution over the set of sampled figure-ground segmentations, the collected segmentations are used to calculate the most uncertain region to ask the label from the user. Chen @cite_21 select the query-pixel with the highest uncertainty referred to the transductive inference measurement.
@cite_41 @cite_21 @cite_3 @cite_5 @cite_13 @cite_17
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This work addresses the problem of segmenting an object of interest out of a video. We show that video object segmentation can be naturally cast as a semi-supervised learning problem and be efficiently solved using harmonic functions. We propose an incremental self-training approach by iteratively labeling the least uncertain frame and updating similarity metrics. Our self-training video segmentation produces superior results both qualitatively and quantitatively. Moreover, usage of harmonic functions naturally supports interactive segmentation. We suggest active learning methods for providing guidance to user on what to annotate in order to improve labeling efficiency. We present experimental results using a ground truth data set and a quantitative comparison to a representative object segmentation system. This paper presents an efficient algorithm for interactive image segmentation that responds to 1-bit user feedback. The goal of this type of segmentation is to propose a sequence of yes-or-no questions to the user. Then, according to the 1-bit answers from the user, the segmentation algorithm progressively revises the questions and the segments, so that the segmentation result can approach the ideal region of interest (ROI) in the mind of the user. We define a question as an event that whether a chosen superpixel hits the ROI or not. In general, an interactive image segmentation algorithm is better to achieve high segmentation accuracy, low response time, and simple manipulation. We fulfill these demands by designing an efficient interactive segmentation algorithm from 1-bit user feedback. Our algorithm employs techniques from over-segmentation, entropy calculation, and transductive inference. Over-segmentation reduces the solution set of questions and the computational costs of transductive inference. Entropy calculation provides a way to characterize the query order of superpixels. Transductive inference is used to estimate the similarity between superpixels and to partition the superpixels into ROI and region of uninterest (ROU). Following the clues from the similarity between superpixels, we design the query-superpixel selection mechanism for human-machine interaction. Our key idea is to narrow down the solution set of questions, and then to propose the most informative question based on the clues of the similarities among the superpixels. We assess our method on four publicly available datasets. The experiments demonstrate that our method provides a plausible solution to the problem of interactive image segmentation with merely 1-bit user feedback. This paper presents an active-learning algorithm for piecewise planar 3D reconstruction of a scene. While previous interactive algorithms require the user to provide tedious interactions to identify all the planes in the scene, we build on successful ideas from the automatic algorithms and introduce the idea of active learning, thereby improving the reconstructions while considerably reducing the effort. Our algorithm first attempts to obtain a piecewise planar reconstruction of the scene automatically through an energy minimization framework. The proposed active-learning algorithm then uses intuitive cues to quantify the uncertainty of the algorithm and suggest regions, querying the user to provide support for the uncertain regions via simple scribbles. These interactions are used to suitably update the algorithm, leading to better reconstructions. We show through machine experiments and a user study that the proposed approach can intelligently query users for interactions on informative regions, and users can achieve better reconstructions of the scene faster, especially for scenes with texture-less surfaces lacking cues like lines which automatic algorithms rely on. Watershed cuts are among the fastest segmentation algorithms and therefore well suited for interactive segmentation of very large 3D data sets. To minimize the number of user interactions (“seeds”) required until the result is correct, we want the computer to actively query the human for input at the most critical locations, in analogy to active learning. These locations are found by means of suitable uncertainty measures. We propose various such measures for watershed cuts along with a theoretical analysis of some of their properties. Extensive evaluation on two types of 3D electron microscopic volumes of neural tissue shows that measures which estimate the non-local consequences of new user inputs achieve performance close to an oracle endowed with complete knowledge of the ground truth. Consider the following scenario between a human user and the computer. Given an image, the user thinks of an object to be segmented within this picture, but is only allowed to provide binary inputs to the computer (yes or no). In these conditions, can the computer guess this hidden segmentation by asking well-chosen questions to the user? We introduce a strategy for the computer to increase the accuracy of its guess in a minimal number of questions. At each turn, the current belief about the answer is encoded in a Bayesian fashion via a probability distribution over the set of all possible segmentations. To efficiently handle this huge space, the distribution is approximated by sampling representative segmentations using an adapted version of the Metropolis-Hastings algorithm, whose proposal moves build on a geodesic distance transform segmentation method. Following a dichotomic search, the question halving the weighted set of samples is finally picked, and the provided answer is used to update the belief for the upcoming rounds. The performance of this strategy is assessed on three publicly available datasets with diverse visual properties. Our approach shows to be a tractable and very adaptive solution to this problem. This paper presents an algorithm for Interactive Co-segmentation 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 user cannot be expected to exhaustively examine all cutouts (from tens of images) returned by the system to make corrections. Hence, we propose iCoseg, an automatic recommendation system that intelligently recommends where the user should scribble next. We introduce and make publicly available the largest co-segmentation datasetyet, the CMU-Cornell iCoseg Dataset, with 38 groups, 643 images, and pixelwise hand-annotated groundtruth. Through machine experiments and real user studies with our developed interface, we show that iCoseg can intelligently recommend regions to scribble on, and users following these recommendations can achieve good quality cutouts with significantly lower time and effort than exhaustively examining all cutouts.