Most existing methods for object segmentation in computer vision are formulated as a labeling task. This, in general, could be transferred to a pixel-wise label assignment task, which is quite similar to the structure of hidden Markov random field. In terms of Markov random field, each pixel can be regarded as a state and has a transition probability to its neighbor pixel, the label behind each pixel is a latent variable and has an emission probability from its corresponding state. In this paper, we reviewed several modern image labeling methods based on Markov random field and conditional random Field. And we compare the result of these methods with some classical image labeling methods. The experiment demonstrates that the introduction of Markov random field and conditional random field make a big difference in the segmentation result.
One response to image labeling task is traditional unsupervised methods involving all kinds of low level information. By measuring color @cite_22 and texture similarity, lots of attempts involving clustering methods such as k-means @cite_20 and mean-shift @cite_16 try to group the local region with higher similarity and assign them with a same label. For computational convenience, superpixel @cite_27 @cite_9 approach is often used as pre-processing step to group the potential similar pixel together. Then all previous approach works on the superpixel group instead of individual pixel. But the problem of these approach occurs due to the use of only local information. So research work starts to combine global and local context to get a better sense of semantic information in the image. In @cite_19 a multi-scale structure is used for segmenting the image based on edge detection with different scale level. In @cite_25 @cite_5 , combination of global context and local informationis proved to be very useful in segmentation task.
@cite_22 @cite_9 @cite_19 @cite_27 @cite_5 @cite_16 @cite_25 @cite_20
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To aid an automatic taxiing system for unmanned aircraft, this paper presents a colour based method for semantic segmentation and image classification in an aerodrome environment with the intention to use the classification output to aid navigation and collision avoidance. Based on previous work, this machine vision system uses semantic segmentation to interpret the scene. Following an initial superpixel based segmentation procedure, a colour based Bayesian Network classifier is trained and used to semantically classify each segmented cluster. HSV colourspace is adopted as it is close to the way of human vision perception of the world, and each channel shows significant differentiation between classes. Luminance is used to identify surface lines on the taxiway, which is then fused with colour classification to give improved classification results. The classification performance of the proposed colour based classifier is tested in a real aerodrome, which demonstrates that the proposed method outperforms a previously developed texture only based method. In this paper, we propose a novel multi-scale edge detection and vector field design scheme. We show that using multiscale techniques edge detection and segmentation quality on natural images can be improved significantly. Our approach eliminates the need for explicit scale selection and edge tracking. Our method favors edges that exist at a wide range of scales and localize these edges at finer scales. This work is then extended to multi-scale image segmentation using our anisotropic diffusion scheme. Grouping cues can affect the performance of segmentation greatly. In this paper, we show that superpixels (image segments) can provide powerful grouping cues to guide segmentation, where superpixels can be collected easily by (over)-segmenting the image using any reasonable existing segmentation algorithms. Generated by different algorithms with varying parameters, superpixels can capture diverse and multi-scale visual patterns of a natural image. Successful integration of the cues from a large multitude of superpixels presents a promising yet not fully explored direction. In this paper, we propose a novel segmentation framework based on bipartite graph partitioning, which is able to aggregate multi-layer superpixels in a principled and very effective manner. Computationally, it is tailored to unbalanced bipartite graph structure and leads to a highly efficient, linear-time spectral algorithm. Our method achieves significantly better performance on the Berkeley Segmentation Database compared to state-of-the-art techniques. A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance. We present a technique for adding global context to deep convolutional networks for semantic segmentation. The approach is simple, using the average feature for a layer to augment the features at each location. In addition, we study several idiosyncrasies of training, significantly increasing the performance of baseline networks (e.g. from FCN). When we add our proposed global feature, and a technique for learning normalization parameters, accuracy increases consistently even over our improved versions of the baselines. Our proposed approach, ParseNet, achieves state-of-the-art performance on SiftFlow and PASCAL-Context with small additional computational cost over baselines, and near current state-of-the-art performance on PASCAL VOC 2012 semantic segmentation with a simple approach. Code is available at this https URL .