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Highlights • We find an observation important to the LSN subspace segmentation. • We provide the theoretical support of the mentioned observation. • Our paper is the first to adopt infinite norm in subspace segmentation. • The computational complexity of our ADM algorithm is lower than O (n 3). • Our method achieve the state-of-the-art results in extensive experiments. Abstract Spectral-clustering based methods have recently attracted considerable attention in the field of subspace segmentation. The approximately block-diagonal graphs achieved by this kind of methods usually contain some noise, i.e., nonzero elements in the off-diagonal region, due to outlier contamination or complex intrinsic structure of the dataset. In the experiment of most previous work, the number of the subspaces is often no more than 10. In this situation, this kind of noise almost has no influence on the segmentation results. However, the segmentation performance could be negatively affected by the noise when the number of subspaces is large, which is quite common in the real-world applications. In this paper, we address the problem of LSN subspace segmentation, i.e., large subspace number subspace segmentation. We first show that the approximately block-diagonal graph with the smaller difference in its diagonal blocks will be more robust to the off-diagonal noise mentioned above. Then, by using the infinity norm to control the bound of the difference in the diagonal blocks, we propose infinity norm minimization for LSN subspace segmentation. Experimental results demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Highlights • A generalized 2D multi-level thresholding criterion function is proved rigorously by mathematical induction method. • A multi-level thresholding scheme for a RGB color image is proposed. • PSO algorithm is applied to seek to optimal threshold values in a very reasonable computational time. • The segmented image is compared with the human segmentation from BSDS300 to evaluate the experiment results quantitatively and objectively. Abstract In this paper, we propose a multi-level thresholding model based on gray-level & local-average histogram (GLLA) and Tsallis–Havrda–Charvát entropy for RGB color image. We validate the multi-level thresholding formulation by using the mathematical induction method. We apply particle swarm optimization (PSO) algorithm to obtain the optimal threshold values for each component of a RGB image. By assigning the mean values from each thresholded class, we obtain three segmented component images independently. We conduct the experiments extensively on The Berkeley Segmentation Dataset and Benchmark (BSDS300) and calculate the average four performance indices (BDE, PRI, GCE and VOI) to show the effectiveness and reasonability of the proposed method. [ABSTRACT FROM AUTHOR]

Graph-based image segmentation is popular, because graphs can naturally represent image parts and the relationships between them. Whilst many single-scale approaches exist, significant interest has been shown in segmentation hierarchies, which represent image objects at different scales. However, segmenting arbitrary images automatically remains elusive: segmentation is under-specified, with different users expecting different outcomes. Hierarchical segmentation compounds this, since it is unclear where in the hierarchy objects should appear. Users can easily edit flat segmentations to influence the outcome, but editing hierarchical segmentations is harder: indeed, many existing interactive editing techniques make only small, local hierarchy changes. In this paper, we address this by introducing ‘zipping’ operations for segmentation hierarchies to facilitate user interaction. We use these operations to implement algorithms for non-sibling node merging and parent switching, and perform experiments on both 2D and 3D images to show that these latter algorithms can significantly reduce the interaction burden on the user. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to measure the correspondence between objects, and exploit it for object and image classification tasks, using the proposed Partial Dominant Orientation Descriptor (PDOD). In particular, the object is represented by a set of stable and informative key locations sampled using Difference of Gaussian. The proposed PDOD at each extracted key location takes into account the position and partially computes the dominant orientation of other key locations relative to it, thus, offering a global distinctive and discriminative characterization. This allows us to learn features that are largely invariant to common image transformations, including changes in object colors and textures. The correspondence in-between two objects is performed by finding for each key location in one object the key location in the other object that has the most similar descriptor. Object classification proceeds by assigning the most relevant category that has maximally similar stored prototype objects to the query object using k -Nearest Neighbors algorithm with Adaptive Object Distance. For efficiency, we further investigate PDOD for image classification by developing powerful image representations based on the popular Bag-of-Words model. The extensive experiments demonstrate that the proposed approach greatly improves the matching and classification results, while achieving the state-of-the-art performances on several challenging benchmark datasets. The obtained results suggest also broader applicability to other classification modalities. [ABSTRACT FROM AUTHOR]

Category-specific object segmentation has been a long-standing research topic in pattern recognition. This paper presents an unsupervised discriminant shape (UDS) to address category-specific object segmentation by incorporating the proposed shape prior into an intuitive energy minimization framework. Recently, based on the region proposal methods, deep Convolutional Neural Networks (CNNs) provide access to candidate segments in categories of interest from images. However, the segments obtained from bottom-up proposals tend to undershoot or overshoot objects and are easily classified into one specific class. To address this problem, we propose an unsupervised discriminant projection based clustering algorithm (UDC) to obtain more precise shape prior to guide the segmentation, and the class-specific proposals are clustered based on their projections onto the discriminant projection direction. Based on the set of proposals, we then obtain the prior information of foreground UDS with an easy voting scheme. The derived UDS prior is finally utilized in the subsequent energy minimizing formulation based figure-ground segmentation. We conduct extensive and comprehensive evaluations on the MSRC, Object Discovery, Fashionista and PASCAL-S datasets, demonstrating the effectiveness and robustness of the UDS based segmentation. [ABSTRACT FROM AUTHOR]

In this paper, the multiple ellipses detection problem on the basis of a data points set coming from a number of ellipses in the plane not known in advance is considered. An ellipse is considered as a Mahalanobis circle with some positive definite matrix. A very efficient method for solving this problem is proposed. This method very successfully combines the well-known direct least squares method and the RANSAC algorithm with a realistic statistical model of multiple ellipses in the plane. The method is illustrated and tested on numerous synthetic and real-world applications. The method was also compared with other similar methods. In the case when a data points set comes from a number of ellipses with clear edges, the proposed method gives results similar to other known methods. However, when a data points set comes from a number of ellipses with noisy edges, the proposed method performs significantly better than the other methods. We should emphasize the advantage and utility of the proposed methods in a variety of applications such as: medical image analysis and ultrasound image segmentation. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic niching clustering algorithm based on individual-connectedness (DNIC) is proposed for unsupervised classification with no prior knowledge. It aims to automatically evolve the optimal number of clusters as well as the cluster centers of the data set based on the proposed adaptive compact k -distance neighborhood algorithm. More specifically, with the adaptive selection of the number of the nearest neighbor and the individual-connectedness algorithm, DNIC often achieves several sets of connecting individuals and each set composes an independent niche. In practice, each set of connecting individuals corresponds to a homogeneous cluster and this ensures the separability of an arbitrary data set theoretically. An application of the DNIC clustering algorithm in color image segmentation is also provided. Experimental results demonstrate that the DNIC clustering algorithm has high performance and flexibility. [ABSTRACT FROM AUTHOR]

We introduce a simple and effective concept for localizing objects in densely cluttered edge images based on shape information. The shape information is characterized by a binary template of the object's contour, provided to search for object instances in the image. We adopt a segment-based search strategy, in which the template is divided into a set of segments. In this work, we propose our own segment representation that we call one-pixel segment (OPS), in which each pixel in the template is treated as a separate segment. This is done to achieve high flexibility that is required to account for intra-class variations. OPS representation can also handle scale changes effectively. A dynamic programming algorithm uses the OPS representation to realize the search process, enabling a detailed localization of the object boundaries in the image. The concept's simplicity is reflected in the ease of implementation, as the paper's title suggests. The algorithm works directly with very noisy edge images extracted using the Canny edge detector, without the need for any preprocessing or learning steps. We present our experiments and show that our results outperform those of very powerful, state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

A competition in unsupervised color image segmentation took place in conjunction with the 22nd International Conference on Pattern Recognition (ICPR 2014). It aimed to promote evaluation of unsupervised color image segmentation algorithms using publicly available data sets, and to allow for any subsequent methods to be easily evaluated and compared with the results of the contested methods under identical conditions. Our comparison of different methods is based on the standard methodology of performance assessment using an on-line verification server. We present in this paper the evaluation of the top six results submitted to the ICPR 2014 contest in unsupervised color image segmentation and compare them with 11 other state-of-the-art unsupervised image segmenters. [ABSTRACT FROM AUTHOR]

Kernel possibilistic fuzzy C-means with local information (KWPFLICM) has important research significance of image segmentation, but it is very sensitive to high noise or outliers. To enhance the segmentation performance of the algorithm, this paper proposes a kernelized total Bregman divergence-driven possibilistic fuzzy clustering with local information (TKWPFLICM). Firstly, a polynomial kernel function is introduced to kernelize total Bregman divergence (TBD), and local neighborhood information of the pixel is used to modify it, which overcomes the shortcomings of Bregman divergence (BD) with rotation variability; Secondly, the modified kernelized TBD and possibilistic typicality are combined to further enhance the anti-noise ability of the algorithm; Finally, the modified kernelized TBD is introduced into the objective function of KWPFLICM algorithm, then a novel robust fuzzy clustering algorithm is derived by optimization theory. Experimental results show that compared with existing fuzzy clustering-related algorithms, the average SA improvement on TKWPFLICM algorithm is in the range of 0.791% to 33.237%. Therefore, TKWPFLICM algorithm has better anti-noise robustness and segmentation accuracy. [ABSTRACT FROM AUTHOR]

• We generalise zipping algorithms for segmentation hierarchy editing to simplify the popular TugGraph visualisation algorithm. • Our SimpleTug approach is modular, straightforward, and saves memory and time compared to existing TugGraph implementations. • We prove its equivalence to other methods, show its effectiveness, and discuss how TugGraph and zipping are connected. • These connections help pin down what TugGraph is really doing, making it easier to reason about in future. • We further show how SimpleTug and an existing TugGraph method can be parallelised. Graphs are an invaluable modelling tool in many domains, but visualising large graphs in their entirety can be difficult. Hierarchical graph visualisation – recursively clustering a graph's nodes to view it at a higher level of abstraction – has thus become popular. However, this can hide important information that a user needs to understand a graph's topology, e.g. nodes' neighbourhoods. TugGraph addressed this by 'separating out' a given node's neighbours from their hierarchy ancestors to visualise them independently. Its original implementation was straightforward, but copied parts of the hierarchy, making it slow and memory-hungry. An optimised later version, which we refer to as FastTug , avoided this, but at a cost in clarity. Optimising TugGraph without sacrificing clarity is difficult because of the need to keep every hierarchy node connected, a common challenge for graph hierarchy editing algorithms. Recently, this problem has been addressed by 'zipping' algorithms, multi-level split/merge algorithms that preserve hierarchy node connectedness and can be built upon for higher-level editing. In this paper, we generalise the original unzipping algorithms to implement SimpleTug , a simple, modular version of TugGraph that is easy to understand and implement, and even faster and more memory-efficient than FastTug. We formally prove its equivalence to FastTug , and show how both can be parallelised. Using our millipede hierarchical image segmentation system, we show experimentally that both the serial and parallel versions of SimpleTug are around 25% faster than their FastTug counterparts, whilst using considerably less memory. Finally, we discuss the interesting theoretical connections between TugGraph and zipping, and suggest ideas for further work. [ABSTRACT FROM AUTHOR]