We adopt convolutional neural networks (CNNs) to be our parametric model to learn discriminative features and classifiers for local patch classification. Based on the occurrence frequency distribution of classes, an ensemble of CNNs (CNN-Ensemble) are learned, in which each CNN component focuses on learning different and complementary visual patterns. The local beliefs of pixels are output by CNN-Ensemble. Considering that visually similar pixels are indistinguishable under local context, we leverage the global scene semantics to alleviate the local ambiguity. The global scene constraint is mathematically achieved by adding a global energy term to the labeling energy function, and it is practically estimated in a non-parametric framework. A large margin-based CNN metric learning method is also proposed for better global belief estimation. In the end, the integration of local and global beliefs gives rise to the class likelihood of pixels, based on which maximum marginal inference is performed to generate the label prediction maps. Even without any post-processing, we achieve the state-of-the-art results on the challenging SiftFlow and Barcelona benchmarks.
Recently, non-parametric label transfer methods @cite_9 @cite_51 @cite_41 @cite_18 @cite_42 @cite_5 have gained popularity due to their outstanding performance and the scalability to large scale data. They usually estimate the class likelihood of image unit from the globally similar images. In a nutshell, global scene features are firstly utilized to retrieve the relevant images, whose label maps are then leveraged to estimate the class likelihood of image units. The pioneering label transfer work @cite_9 transformed RGB image to SIFT @cite_8 image, which was used to seek correspondences over pixels. Then, an energy function was defined over pixel correspondences, and the label prediction maps are obtained by minimizing the energy. The Superparsing system @cite_51 performed label transfer over image superpixels. @cite_41 learned adaptive weights for each low-level features, and it resulted in better nearest neighbor search. @cite_22 @cite_11 built a graph for dense image patch and superpixel to achieve the label transfer. We adopt this framework to evaluate our global energy term. In comparison with these works that are based on hand-crafted features, we used the learned CNN features which are more compact and discriminative. We expect our features to benefit their systems in terms of accuracy and efficiency as well.
@cite_18 @cite_22 @cite_8 @cite_41 @cite_9 @cite_42 @cite_5 @cite_51 @cite_11
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This paper presents a nonparametric approach to semantic parsing using small patches and simple gradient, color and location features. We learn the relevance of individual feature channels at test time using a locally adaptive distance metric. To further improve the accuracy of the nonparametric approach, we examine the importance of the retrieval set used to compute the nearest neighbours using a novel semantic descriptor to retrieve better candidates. The approach is validated by experiments on several datasets used for semantic parsing demonstrating the superiority of the method compared to the state of art approaches. We address the problem of semantic segmentation, or multi-class pixel labeling, by constructing a graph of dense overlapping patch correspondences across large image sets. We then transfer annotations from labeled images to unlabeled images using the established patch correspondences. Unlike previous approaches to non-parametric label transfer our approach does not require an initial image retrieval step. Moreover, we operate on a graph for computing mappings between images, which avoids the need for exhaustive pairwise comparisons. Consequently, we can leverage offline computation to enhance performance at test time. We conduct extensive experiments to analyze different variants of our graph construction algorithm and evaluate multi-class pixel labeling performance on several challenging datasets. This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance. This paper proposes a non-parametric approach to scene parsing inspired by the work of Tighe and Lazebnik [22]. In their approach, a simple kNN scheme with multiple descriptor types is used to classify super-pixels. We add two novel mechanisms: (i) a principled and efficient method for learning per-descriptor weights that minimizes classification error, and (ii) a context-driven adaptation of the training set used for each query, which conditions on common classes (which are relatively easy to classify) to improve performance on rare ones. The first technique helps to remove extraneous descriptors that result from the imperfect distance metrics representations of each super-pixel. The second contribution re-balances the class frequencies, away from the highly-skewed distribution found in real-world scenes. Both methods give a significant performance boost over [22] and the overall system achieves state-of-the-art performance on the SIFT-Flow dataset. In this paper we propose a novel nonparametric approach for object recognition and scene parsing using dense scene alignment. Given an input image, we retrieve its best matches from a large database with annotated images using our modified, coarse-to-fine SIFT flow algorithm that aligns the structures within two images. Based on the dense scene correspondence obtained from the SIFT flow, our system warps the existing annotations, and integrates multiple cues in a Markov random field framework to segment and recognize the query image. Promising experimental results have been achieved by our nonparametric scene parsing system on a challenging database. Compared to existing object recognition approaches that require training for each object category, our system is easy to implement, has few parameters, and embeds contextual information naturally in the retrieval alignment procedure. Scene parsing is the problem of assigning a semantic label to every pixel in an image. Though an ambitious task, impressive advances have been made in recent years, in particular in scalable nonparametric techniques suitable for open-universe databases. This paper presents the CollageParsing algorithm for scalable nonparametric scene parsing. In contrast to common practice in recent nonparametric approaches, CollageParsing reasons about mid-level windows that are designed to capture entire objects, instead of low-level superpixels that tend to fragment objects. On a standard benchmark consisting of outdoor scenes from the LabelMe database, CollageParsing achieves state-of-the-art nonparametric scene parsing results with 7 to 11 higher average per-class accuracy than recent nonparametric approaches. This paper presents a scalable scene parsing algorithm based on image retrieval and superpixel matching. We focus on rare object classes, which play an important role in achieving richer semantic understanding of visual scenes, compared to common background classes. Towards this end, we make two novel contributions: rare class expansion and semantic context description. First, considering the long-tailed nature of the label distribution, we expand the retrieval set by rare class exemplars and thus achieve more balanced superpixel classification results. Second, we incorporate both global and local semantic context information through a feedback based mechanism to refine image retrieval and superpixel matching. Results on the SIFTflow and LMSun datasets show the superior performance of our algorithm, especially on the rare classes, without sacrificing overall labeling accuracy. This paper presents a simple and effective nonparametric approach to the problem of image parsing, or labeling image regions (in our case, superpixels produced by bottom-up segmentation) with their categories. This approach requires no training, and it can easily scale to datasets with tens of thousands of images and hundreds of labels. It works by scene-level matching with global image descriptors, followed by superpixel-level matching with local features and efficient Markov random field (MRF) optimization for incorporating neighborhood context. Our MRF setup can also compute a simultaneous labeling of image regions into semantic classes (e.g., tree, building, car) and geometric classes (sky, vertical, ground). Our system outperforms the state-of-the-art non-parametric method based on SIFT Flow on a dataset of 2,688 images and 33 labels. In addition, we report per-pixel rates on a larger dataset of 15,150 images and 170 labels. To our knowledge, this is the first complete evaluation of image parsing on a dataset of this size, and it establishes a new benchmark for the problem. We present a fast approximate nearest neighbor algorithm for semantic segmentation. Our algorithm builds a graph over superpixels from an annotated set of training images. Edges in the graph represent approximate nearest neighbors in feature space. At test time we match superpixels from a novel image to the training images by adding the novel image to the graph. A move-making search algorithm allows us to leverage the graph and image structure for finding matches. We then transfer labels from the training images to the image under test. To promote good matches between superpixels we propose to learn a distance metric that weights the edges in our graph. Our approach is evaluated on four standard semantic segmentation datasets and achieves results comparable with the state-of-the-art.