The Experts below are selected from a list of 123 Experts worldwide ranked by ideXlab platform
Shawn Newsam - One of the best experts on this subject based on the ideXlab platform.
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spatial pyramid co occurrence for Image classification
International Conference on Computer Vision, 2011Co-Authors: Yi Yang, Shawn NewsamAbstract:We describe a novel Image representation termed spatial pyramid co-occurrence which characterizes both the photometric and geometric aspects of an Image. Specifically, the co-occurrences of visual words are computed with respect to spatial predicates over a hierarchical spatial partitioning of an Image. The representation captures both the absolute and relative spatial arrangement of the words and, through the choice and combination of the predicates, can characterize a variety of spatial relationships. Our representation is motivated by the analysis of overhead Imagery such as from satellites or aircraft. This Imagery generally does not have an absolute reference frame and thus the relative spatial arrangement of the Image elements often becomes the key discriminating feature. We validate this hypothesis using a challenging Ground Truth Image dataset of 21 land-use classes manually extracted from high-resolution aerial Imagery. Our approach is shown to result in higher classification rates than a non-spatial bagof- visual-words approach as well as a popular approach for characterizing the absolute spatial arrangement of visual words, the spatial pyramid representation of Lazebnik et al. [7]. While our primary objective is analyzing overhead Imagery, we demonstrate that our approach achieves state-of-the-art performance on the Graz-01 object class dataset and performs competitively on the 15 Scene dataset.
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bag of visual words and spatial extensions for land use classification
Advances in Geographic Information Systems, 2010Co-Authors: Yi Yang, Shawn NewsamAbstract:We investigate bag-of-visual-words (BOVW) approaches to land-use classification in high-resolution overhead Imagery. We consider a standard non-spatial representation in which the frequencies but not the locations of quantized Image features are used to discriminate between classes analogous to how words are used for text document classification without regard to their order of occurrence. We also consider two spatial extensions, the established spatial pyramid match kernel which considers the absolute spatial arrangement of the Image features, as well as a novel method which we term the spatial co-occurrence kernel that considers the relative arrangement. These extensions are motivated by the importance of spatial structure in geographic data. The methods are evaluated using a large Ground Truth Image dataset of 21 land-use classes. In addition to comparisons with standard approaches, we perform extensive evaluation of different configurations such as the size of the visual dictionaries used to derive the BOVW representations and the scale at which the spatial relationships are considered. We show that even though BOVW approaches do not necessarily perform better than the best standard approaches overall, they represent a robust alternative that is more effective for certain land-use classes. We also show that extending the BOVW approach with our proposed spatial co-occurrence kernel consistently improves performance.
Alan C Bovik - One of the best experts on this subject based on the ideXlab platform.
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a statistical evaluation of recent full reference Image quality assessment algorithms
IEEE Transactions on Image Processing, 2006Co-Authors: Hamid R Sheikh, Muhammad Sabir, Alan C BovikAbstract:Measurement of visual quality is of fundamental importance for numerous Image and video processing applications, where the goal of quality assessment (QA) algorithms is to automatically assess the quality of Images or videos in agreement with human quality judgments. Over the years, many researchers have taken different approaches to the problem and have contributed significant research in this area and claim to have made progress in their respective domains. It is important to evaluate the performance of these algorithms in a comparative setting and analyze the strengths and weaknesses of these methods. In this paper, we present results of an extensive subjective quality assessment study in which a total of 779 distorted Images were evaluated by about two dozen human subjects. The "Ground Truth" Image quality data obtained from about 25 000 individual human quality judgments is used to evaluate the performance of several prominent full-reference Image quality assessment algorithms. To the best of our knowledge, apart from video quality studies conducted by the Video Quality Experts Group, the study presented in this paper is the largest subjective Image quality study in the literature in terms of number of Images, distortion types, and number of human judgments per Image. Moreover, we have made the data from the study freely available to the research community . This would allow other researchers to easily report comparative results in the future
Nouman Ali - One of the best experts on this subject based on the ideXlab platform.
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a novel Image retrieval based on visual words integration of sift and surf
PLOS ONE, 2016Co-Authors: Nouman Ali, Khalid Bashir Bajwa, Savvas A Chatzichristofis, Zeshan Iqbal, Muhammad Rashid, Robert Sablatnig, Hafiz Adnan HabibAbstract:With the recent evolution of technology, the number of Image archives has increased exponentially. In Content-Based Image Retrieval (CBIR), high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level Image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). The two local features representations are selected for Image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination. The visual words integration of SIFT and SURF adds the robustness of both features to Image retrieval. The qualitative and quantitative comparisons conducted on Corel-1000, Corel-1500, Corel-2000, Oliva and Torralba and Ground Truth Image benchmarks demonstrate the effectiveness of the proposed visual words integration.
Yi Yang - One of the best experts on this subject based on the ideXlab platform.
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spatial pyramid co occurrence for Image classification
International Conference on Computer Vision, 2011Co-Authors: Yi Yang, Shawn NewsamAbstract:We describe a novel Image representation termed spatial pyramid co-occurrence which characterizes both the photometric and geometric aspects of an Image. Specifically, the co-occurrences of visual words are computed with respect to spatial predicates over a hierarchical spatial partitioning of an Image. The representation captures both the absolute and relative spatial arrangement of the words and, through the choice and combination of the predicates, can characterize a variety of spatial relationships. Our representation is motivated by the analysis of overhead Imagery such as from satellites or aircraft. This Imagery generally does not have an absolute reference frame and thus the relative spatial arrangement of the Image elements often becomes the key discriminating feature. We validate this hypothesis using a challenging Ground Truth Image dataset of 21 land-use classes manually extracted from high-resolution aerial Imagery. Our approach is shown to result in higher classification rates than a non-spatial bagof- visual-words approach as well as a popular approach for characterizing the absolute spatial arrangement of visual words, the spatial pyramid representation of Lazebnik et al. [7]. While our primary objective is analyzing overhead Imagery, we demonstrate that our approach achieves state-of-the-art performance on the Graz-01 object class dataset and performs competitively on the 15 Scene dataset.
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bag of visual words and spatial extensions for land use classification
Advances in Geographic Information Systems, 2010Co-Authors: Yi Yang, Shawn NewsamAbstract:We investigate bag-of-visual-words (BOVW) approaches to land-use classification in high-resolution overhead Imagery. We consider a standard non-spatial representation in which the frequencies but not the locations of quantized Image features are used to discriminate between classes analogous to how words are used for text document classification without regard to their order of occurrence. We also consider two spatial extensions, the established spatial pyramid match kernel which considers the absolute spatial arrangement of the Image features, as well as a novel method which we term the spatial co-occurrence kernel that considers the relative arrangement. These extensions are motivated by the importance of spatial structure in geographic data. The methods are evaluated using a large Ground Truth Image dataset of 21 land-use classes. In addition to comparisons with standard approaches, we perform extensive evaluation of different configurations such as the size of the visual dictionaries used to derive the BOVW representations and the scale at which the spatial relationships are considered. We show that even though BOVW approaches do not necessarily perform better than the best standard approaches overall, they represent a robust alternative that is more effective for certain land-use classes. We also show that extending the BOVW approach with our proposed spatial co-occurrence kernel consistently improves performance.
Rajaa Touahni - One of the best experts on this subject based on the ideXlab platform.
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building roof segmentation from aerial Images using a lineand region based watershed segmentation technique
Sensors, 2015Co-Authors: Youssef El Merabet, Cyril Meurie, Yassine Ruichek, Abderrahmane Sbihi, Rajaa TouahniAbstract:In this paper, we present a novel strategy for roof segmentation from aerial Images (orthophotoplans) based on the cooperation of edge- and region-based segmentation methods. The proposed strategy is composed of three major steps. The first one, called the pre-processing step, consists of simplifying the acquired Image with an appropriate couple of invariant and gradient, optimized for the application, in order to limit illumination changes (shadows, brightness, etc.) affecting the Images. The second step is composed of two main parallel treatments: on the one hand, the simplified Image is segmented by watershed regions. Even if the first segmentation of this step provides good results in general, the Image is often over-segmented. To alleviate this problem, an efficient region merging strategy adapted to the orthophotoplan particularities, with a 2D modeling of roof ridges technique, is applied. On the other hand, the simplified Image is segmented by watershed lines. The third step consists of integrating both watershed segmentation strategies into a single cooperative segmentation scheme in order to achieve satisfactory segmentation results. Tests have been performed on orthophotoplans containing 100 roofs with varying complexity, and the results are evaluated with the VINETcriterion using Ground-Truth Image segmentation. A comparison with five popular segmentation techniques of the literature demonstrates the effectiveness and the reliability of the proposed approach. Indeed, we obtain a good segmentation rate of 96% with the proposed method compared to 87.5% with statistical region merging (SRM), 84% with mean shift, 82% with color structure code (CSC), 80% with efficient graph-based segmentation algorithm (EGBIS) and 71% with JSEG.
