Abstract Semantic segmentation (i.e. image parsing) aims to annotate each image pixel with its corresponding semantic class label. Spatially consistent labeling of the image requires an accurate description and modeling of the local contextual information. Segmentation result is typically improved by Markov Random Field (MRF) optimization on the initial labels. However this improvement is limited by the accuracy of initial result and how the contextual neighborhood is defined. In this paper, we develop generalized and flexible contextual models for segmentation neighborhoods in order to improve parsing accuracy. Instead of using a fixed segmentation and neighborhood definition, we explore various contextual models for fusion of complementary information available in alternative segmentations of the same image. In other words, we propose a novel MRF framework that describes and optimizes the contextual dependencies between multiple segmentations. Simulation results on two common datasets demonstrate significant improvement in parsing accuracy over the baseline approaches.
The main novelty of this paper is the fusion of multiple parsing methods within MRF formalism. @cite_7 also labels segments by late fusion of SVM classifiers over multiple segmentations; however, fusion is simply performed by taking the mean max multiplication of classifier probabilities in intersecting regions and label smoothing by relaxation labeling is treated as a post-processing step on the fused result. Methods such as @cite_31 , @cite_11 , @cite_37 define hierarchical MRF models over multiple segmentations but do not consider segmentations and class scores coming from alternative methods. In these approaches, since the segmentations and their unary potentials at different levels of the hierarchy are not independently generated, there will be no significant complementary information for fusion over the hierarchical MRF. As a result, gains in labeling accuracy are limited. On the other hand, our MRF framework allows for the fusion of independent segmentations and class likelihoods coming from much different classifiers.
@cite_31 @cite_37 @cite_7 @cite_11
2301304385 2737171127 2075695045 2137881638
Abstract We propose a novel label inference approach for segmenting natural images into perceptually meaningful regions. Each pixel is assigned a serial label indicating its category using a Markov Random Field (MRF) model. To this end, we introduce a framework for latent semantic inference of serial labels, called LSI, by integrating local pixel, global region, and scale information of an natural image into a MRF-inspired model. The key difference from traditional MRF based image segmentation methods is that we infer semantic segments in the label space instead of the pixel space. We first design a serial label formation algorithm named Color and Location Density Clustering (CLDC) to capture the local pixel information. Then we propose a label merging strategy to combine global cues of labels in the Cross-Region potential to grasp the contextual information within an image. In addition, to align with the structure of segmentation, a hierarchical label alignment mechanism is designed to formulate the Cross-Scale potential by utilizing the scale information to catch the hierarchy of image at different scales for final segmentation optimization. We evaluate the performance of the proposed approach on the Berkeley Segmentation Dataset and preferable results are achieved. Automatic image annotation and image segmentation are two prominent research fields of Computer Vision, that are getting higher attention these days to accomplish image analysis and scene understanding. In this work, we present an annotation algorithm based on a hierarchical image partition, that makes use of Markov Random Fields (MRFs) to model spatial and hierarchical relations among regions in the image. In this way, we can capture local, global and contextual information. Also, we combine the processes of annotation and segmentation in an iterative way so that each process benefits from the other. Furthermore, we investigate the selection of the starting segmentation level for the hierarchical annotation process, to show its relevance for the final results. We experimentally validate our approach in three well-known datasets: CorelA, Stanford Background and MSRC-21 datasets. In these datasets, we achieved better or comparable results to other state-of-the-art algorithms, improving our base classifier ... In this paper we study the problem of the detection of semantic objects from known categories in images. Unlike existing techniques which operate at the pixel or at a patch level for recognition, we propose to rely on the categorization of image segments. Recent work has highlighted that image segments provide a sound support for visual object class recognition. In this work, we use image segments as primitives to extract robust features and train detection models for a predefined set of categories. Several segmentation algorithms are benchmarked and their performances for segment recognition are compared. We then propose two methods for enhancing the segments classification, one based on the fusion of the classification results obtained with the different segmentations, the other one based on the optimization of the global labelling by correcting local ambiguities between neighbor segments. We use as a benchmark the Microsoft MSRC-21 image database and show that our method competes with the current state-of-the-art. In this paper we propose an approach to holistic scene understanding that reasons jointly about regions, location, class and spatial extent of objects, presence of a class in the image, as well as the scene type. Learning and inference in our model are efficient as we reason at the segment level, and introduce auxiliary variables that allow us to decompose the inherent high-order potentials into pairwise potentials between a few variables with small number of states (at most the number of classes). Inference is done via a convergent message-passing algorithm, which, unlike graph-cuts inference, has no submodularity restrictions and does not require potential specific moves. We believe this is very important, as it allows us to encode our ideas and prior knowledge about the problem without the need to change the inference engine every time we introduce a new potential. Our approach outperforms the state-of-the-art on the MSRC-21 benchmark, while being much faster. Importantly, our holistic model is able to improve performance in all tasks.