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Yayın Multi-hypothesis contextual modeling for semantic segmentation(Elsevier Science BV, 2019-01-01) Ateş, Hasan Fehmi; Sünetci, SercanSemantic 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.Yayın Kernel likelihood estimation for superpixel image parsing(Springer Verlag, 2016) Ateş, Hasan Fehmi; Sünetci, Sercan; Ak, Kenan EmirIn superpixel-based image parsing, the image is first segmented into visually consistent small regions, i.e. superpixels; then superpixels are parsed into different categories. SuperParsing algorithm provides an elegant nonparametric solution to this problem without any need for classifier training. Superpixels are labeled based on the likelihood ratios that are computed from class conditional density estimates of feature vectors. In this paper, local kernel density estimation is proposed to improve the estimation of likelihood ratios and hence the labeling accuracy. By optimizing kernel bandwidths for each feature vector, feature densities are better estimated especially when the set of training samples is sparse. The proposed method is tested on the SIFT Flow dataset consisting of 2,688 images and 33 labels, and is shown to outperform SuperParsing and some of its extended versions in terms of classification accuracy.Yayın Improving semantic segmentation with generalized models of local context(Springer International Publishing AG, 2017) Ateş, Hasan Fehmi; Sünetci, SercanSemantic 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. Superpixel image parsing methods provide this consistency by carrying out labeling at the superpixel-level based on superpixel features and neighborhood information. In this paper, we develop generalized and flexible contextual models for superpixel neighborhoods in order to improve parsing accuracy. Instead of using a fixed segmentation and neighborhood definition, we explore various contextual models to combine complementary information available in alternative superpixel segmentations of the same image. Simulation results on two datasets demonstrate significant improvement in parsing accuracy over the baseline approach.Yayın Evaluation of feature selection and encoding methods for superpixel image parsing(Işık Üniversitesi, 2017-12-14) Sünetci, Sercan; Ateş, Hasan Fehmi; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Elektronik Mühendisliği Yüksek Lisans ProgramıThis thesis is about image parsing which is one of the important problems in computer vision. The goal of image parsing is segmentation of object and labeling of each object. Recently, a popular way of image segmentation and classifcation is superpixels. Image is segmented into visually logical small regions by using superpixel algorithm and then, superpixels are parsed into diferent classes. Classifcation performance is signifcantly afected by the properties of superpixel algorithm and parametric settings. SuperParsing is one of the superpixel-based image parsing algorithm and provides a succesful nonparametric solution for image segmentation and classifcation problem without any need for classifer training. SuperParsing labels each superpixel based on feature matching between the superpixel and a subset of the training superpixels. The training subset is determined by global matching between the test image and the training set. For superpixel matching the method makes use of a rich set of superpixel features. Class conditional log-likelihood is computed based on these matched features. The main objective of this thesis is to show improvements in labeling accuracy percentage by using feature encoding and selection methods, including learned features from Convolutional Neural Network (CNN) models. We perform two different encoding methods to selected features of superpixels and show that feature encoding improves parsing accuracy. The applied feature encoding methods are locality-constrained linear encoding (LLC) and kernel codebook encoding (KCB). LLC encoding method gives us 2:6% improvement on per-pixel accuracy for SIFT Flow dataset and 6:8% improvement on per-pixel accuracy for 19-class LabelMe dataset. KCB encoding method gives us 3:6% improvement on per-pixel accuracy for SIFT Flow dataset and 6:2% improvement on per-pixel accuracy for 19-class LabelMe dataset. All these results are overall improvement which are computed over original SuperParsing. Most recent studies about image segmentation and classifcation use CNN tiioi improve their accuracy percentage. Features extracted from pre-trained networks, which are trained on large image databases, can be used in addition to handcrafted features in image segmentation. Last layer of these CNN models give the best features for classifcation. We test learned CNN features together with KCB or LLC encoding methods. We use CNN features both for global matching and superpixel matching. These tests give us 7:3% overall improvement over originalSuperParsing on SIFT Flow dataset and 10:3% overall improvement over original SuperParsing on 19-class LabelMe dataset.
