123 research outputs found
Unsupervised Object Discovery and Localization in the Wild: Part-based Matching with Bottom-up Region Proposals
This paper addresses unsupervised discovery and localization of dominant
objects from a noisy image collection with multiple object classes. The setting
of this problem is fully unsupervised, without even image-level annotations or
any assumption of a single dominant class. This is far more general than
typical colocalization, cosegmentation, or weakly-supervised localization
tasks. We tackle the discovery and localization problem using a part-based
region matching approach: We use off-the-shelf region proposals to form a set
of candidate bounding boxes for objects and object parts. These regions are
efficiently matched across images using a probabilistic Hough transform that
evaluates the confidence for each candidate correspondence considering both
appearance and spatial consistency. Dominant objects are discovered and
localized by comparing the scores of candidate regions and selecting those that
stand out over other regions containing them. Extensive experimental
evaluations on standard benchmarks demonstrate that the proposed approach
significantly outperforms the current state of the art in colocalization, and
achieves robust object discovery in challenging mixed-class datasets.Comment: CVPR 201
A mutual GrabCut method to solve co-segmentation
Extent: 11 p.Co-segmentation aims at segmenting common objects from a group of images. Markov random field (MRF) has been widely used to solve co-segmentation, which introduces a global constraint to make the foreground similar to each other. However, it is difficult to minimize the new model. In this paper, we propose a new Markov random field-based co-segmentation model to solve co-segmentation problem without minimization problem. In our model, foreground similarity constraint is added into the unary term of MRF model rather than the global term, which can be minimized by graph cut method. In the model, a new energy function is designed by considering both the foreground similarity and the background consistency. Then, a mutual optimization approach is used to minimize the energy function. We test the proposed method on many pairs of images. The experimental results demonstrate the effectiveness of the proposed method.Zhisheng Gao, Peng Shi, Hamid Reza Karimi and Zheng Pe
CG2Real: Improving the Realism of Computer Generated Images using a Large Collection of Photographs
Computer Graphics (CG) has achieved a high level of realism, producing strikingly vivid images. This realism, however, comes at the cost of long and often expensive manual modeling, and most often humans can still distinguish between CG images and real images. We present a novel method to make CG images look more realistic that is simple and accessible to novice users. Our system uses a large collection of photographs gathered from online repositories. Given a CG image, we retrieve a small number of real images with similar global structure. We identify corresponding regions between the CG and real images using a novel mean-shift cosegmentation algorithm. The user can then automatically transfer color, tone, and texture from matching regions to the CG image. Our system only uses image processing operations and does not require a 3D model of the scene, making it fast and easy to integrate into digital content creation workflows. Results of a user study show that our improved CG images appear more realistic than the originals
Interactive Cosegmentation Using Global and Local Energy Optimization
We propose a novel interactive cosegmentation method using global and local energy optimization. The global energy includes two terms: 1) the global scribbled energy and 2) the interimage energy. The first one utilizes the user scribbles to build the Gaussian mixture model and improve the cosegmentation performance. The second one is a global constraint, which attempts to match the histograms of common objects. To minimize the local energy, we apply the spline regression to learn the smoothness in a local neighborhood. This energy optimization can be converted into a constrained quadratic programming problem. To reduce the computational complexity, we propose an iterative optimization algorithm to decompose this optimization problem into several subproblems. The experimental results show that our method outperforms the state-of-the-art unsupervised cosegmentation and interactive cosegmentation methods on the iCoseg and MSRC benchmark data sets
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Human machine collaboration for foreground segmentation in images and videos
Foreground segmentation is defined as the problem of generating pixel level foreground masks for all the objects in a given image or video. Accurate foreground segmentations in images and videos have several potential applications such as improving search, training richer object detectors, image synthesis and re-targeting, scene and activity understanding, video summarization, and post-production video editing.
One effective way to solve this problem is human-machine collaboration. The main idea is to let humans guide the segmentation process through some partial supervision. As humans, we are extremely good at perception and can easily identify the foreground regions. Computers, on the other hand, lack this capability, but are extremely good at continuously processing large volumes of data at the lowest level of detail with great efficiency. Bringing these complementary strengths together can lead to systems which are accurate and cost-effective at the same time. However, in any such human-machine collaboration system, cost effectiveness and higher accuracy are competing goals. While more involvement from humans can certainly lead to higher accuracy, it also leads to increased cost both in terms of time and money. On the other hand, relying more on machines is cost-effective, but algorithms are still nowhere near human-level performance. Balancing this cost versus accuracy trade-off holds the key behind success for such a hybrid system.
In this thesis, I develop foreground segmentation algorithms which effectively and efficiently make use of human guidance for accurately segmenting foreground objects in images and videos. The algorithms developed in this thesis actively reason about the best modalities or interactions through which a user can provide guidance to the system for generating accurate segmentations. At the same time, these algorithms are also capable of prioritizing human guidance on instances where it is most needed. Finally, when structural similarity exists within data (e.g., adjacent frames in a video or similar images in a collection), the algorithms developed in this thesis are capable of propagating information from instances which have received human guidance to the ones which did not. Together, these characteristics result in a substantial savings in human annotation cost while generating high quality foreground segmentations in images and videos.
In this thesis, I consider three categories of segmentation problems all of which can greatly benefit from human-machine collaboration. First, I consider the problem of interactive image segmentation. In traditional interactive methods a human annotator provides a coarse spatial annotation (e.g., bounding box or freehand outlines) around the object of interest to obtain a segmentation. The mode of manual annotation used affects both its accuracy and ease-of-use. Whereas existing methods assume a fixed form of input no matter the image, in this thesis I propose a data-driven algorithm which learns whether an interactive segmentation method will succeed if initialized with a given annotation mode. This allows us to predict the modality that will be sufficiently strong to yield a high quality segmentation for a given image and results in large savings in annotation costs. I also propose a novel interactive segmentation algorithm called Click Carving which can accurately segment objects in images and videos using a very simple form of human interaction---point clicks. It outperforms several state-of-the-art methods and requires only a fraction of human effort in comparison.
Second, I consider the problem of segmenting images in a weakly supervised image collection. Here, we are given a collection of images all belonging to the same object category and the goal is to jointly segment the common object from all the images. For this, I develop a stagewise active approach to segmentation propagation: in each stage, the images that appear most valuable for human annotation are actively determined and labeled by human annotators, then the foreground estimates are revised in all unlabeled images accordingly. In order to identify images that, once annotated, will propagate well to other examples, I introduce an active selection procedure that operates on the joint segmentation graph over all images. It prioritizes human intervention for those images that are uncertain and influential in the graph, while also mutually diverse. Building on this, I also introduce the problem of measuring compatibility between image pairs for joint segmentation. I show that restricting the joint segmentation to only compatible image pairs results in an improved joint segmentation performance.
Finally, I propose a semi-supervised approach for segmentation propagation in video. Given human supervision in some frames of a video, this information can be propagated through time. The main challenge is that the foreground object may move quickly in the scene at the same time its appearance and shape evolves over time. To address this, I propose a higher order supervoxel label consistency potential which leverages bottom-up supervoxels to enforce long-range temporal consistency during propagation. I also introduce the notion of a generic pixel-level objectness in images and videos by training a deep neural network which uses appearance and motion to automatically assign a score to each pixel capturing its likelihood to be an "object" or "background". I show that the human guidance in the semi-supervised propagation algorithm can be further augmented with the generic pixel-objectness scores to obtain an even more accurate foreground segmentation in videos.
Throughout, I provide extensive evaluation on challenging datasets and also compare with many state-of-the-art methods and other baselines validating the strengths of proposed algorithms. The outcomes across several different experiments show that the proposed human-machine collaboration algorithms achieve accurate segmentation of foreground objects in images and videos while saving a large amount of human annotation effort.Computer Science
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