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Image-Level Structure Recognition Using Image Features, Templates, and Ensemble of Classifiers. Symmetry (Basel) 2020. [DOI: 10.3390/sym12071072] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Image-level structural recognition is an important problem for many applications of computer vision such as autonomous vehicle control, scene understanding, and 3D TV. A novel method, using image features extracted by exploiting predefined templates, each associated with individual classifier, is proposed. The template that reflects the symmetric structure consisting of a number of components represents a stage—a rough structure of an image geometry. The following image features are used: a histogram of oriented gradient (HOG) features showing the overall object shape, colors representing scene information, the parameters of the Weibull distribution features, reflecting relations between image statistics and scene structure, and local binary pattern (LBP) and entropy (E) values representing texture and scene depth information. Each of the individual classifiers learns a discriminative model and their outcomes are fused together using sum rule for recognizing the global structure of an image. The proposed method achieves an 86.25% recognition accuracy on the stage dataset and a 92.58% recognition rate on the 15-scene dataset, both of which are significantly higher than the other state-of-the-art methods.
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Self-propagating video segmentation using patch matching and enhanced Onecut. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-2033-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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Noorie Z, Afsari F. Sparse feature selection: Relevance, redundancy and locality structure preserving guided by pairwise constraints. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2019.105956] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Optimized OpenCL™ kernels for frequency domain image high-boost filters using image vectorization technique. SN APPLIED SCIENCES 2019. [DOI: 10.1007/s42452-019-1445-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
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Ambika PS, Rajendrakumar PK, Ramchand R. Mode determination in variational mode decomposition and its application in fault diagnosis of rolling element bearings. SN APPLIED SCIENCES 2019. [DOI: 10.1007/s42452-019-1005-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
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Abstract
In recent years researchers have worked to understand image contents in computer vision. In particular, the bag of visual words (BoVW) model, which describes images in terms of a frequency histogram of visual words, is the most adopted paradigm. The main drawback is the lack of information about location and the relationships between features. For this purpose, we propose a new paradigm called bag of ARSRG (attributed relational SIFT (scale-invariant feature transform) regions graph) words (BoAW). A digital image is described as a vector in terms of a frequency histogram of graphs. Adopting a set of steps, the images are mapped into a vector space passing through a graph transformation. BoAW is evaluated in an image classification context on standard datasets and its effectiveness is demonstrated through experimental results compared with well-known competitors.
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Ali N, Zafar B, Iqbal MK, Sajid M, Younis MY, Dar SH, Mahmood MT, Lee IH. Modeling global geometric spatial information for rotation invariant classification of satellite images. PLoS One 2019; 14:e0219833. [PMID: 31323065 PMCID: PMC6641163 DOI: 10.1371/journal.pone.0219833] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 07/02/2019] [Indexed: 11/28/2022] Open
Abstract
The classification of high-resolution satellite images is an open research problem for computer vision research community. In last few decades, the Bag of Visual Word (BoVW) model has been used for the classification of satellite images. In BoVW model, an orderless histogram of visual words without any spatial information is used as image signature. The performance of BoVW model suffers due to this orderless nature and addition of spatial clues are reported beneficial for scene and geographical classification of images. Most of the image representations that can compute image spatial information as are not invariant to rotations. A rotation invariant image representation is considered as one of the main requirement for satellite image classification. This paper presents a novel approach that computes the spatial clues for the histograms of BoVW model that is robust to the image rotations. The spatial clues are calculated by computing the histograms of orthogonal vectors. This is achieved by calculating the magnitude of orthogonal vectors between Pairs of Identical Visual Words (PIVW) relative to the geometric center of an image. The comparative analysis is performed with recently proposed research to obtain the best spatial feature representation for the satellite imagery. We evaluated the proposed research for image classification using three standard image benchmarks of remote sensing. The results and comparisons conducted to evaluate this research show that the proposed approach performs better in terms of classification accuracy for a variety of datasets based on satellite images.
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Affiliation(s)
- Nouman Ali
- Department of Software Engineering, Mirpur University of Science & Technology (MUST), Mirpur AJK, Pakistan
| | - Bushra Zafar
- Department of Computer Science, National Textile University, Faisalabad, Pakistan
- Department of Computer Science, Government College University, Faisalabad, Pakistan
| | | | - Muhammad Sajid
- Department of Electrical Engineering, Mirpur University of Science & Technology (MUST), Mirpur AJK, Pakistan
| | - Muhammad Yamin Younis
- Department of Mechanical Engineering, Mirpur University of Science & Technology (MUST), Mirpur AJK, Pakistan
| | - Saadat Hanif Dar
- Department of Software Engineering, Mirpur University of Science & Technology (MUST), Mirpur AJK, Pakistan
| | - Muhammad Tariq Mahmood
- School of Computer Science and Engineering, Korea University of Technology and Education, Cheonan, South Korea
| | - Ik Hyun Lee
- Department of Mechatronics, Korea Polytechnic University, Siheung-si, Gyeonggi-do, South Korea
- * E-mail:
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Fernandez-Lozano C, Carballal A, Machado P, Santos A, Romero J. Visual complexity modelling based on image features fusion of multiple kernels. PeerJ 2019; 7:e7075. [PMID: 31346494 PMCID: PMC6642794 DOI: 10.7717/peerj.7075] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 05/04/2019] [Indexed: 01/28/2023] Open
Abstract
Humans' perception of visual complexity is often regarded as one of the key principles of aesthetic order, and is intimately related to the physiological, neurological and, possibly, psychological characteristics of the human mind. For these reasons, creating accurate computational models of visual complexity is a demanding task. Building upon on previous work in the field (Forsythe et al., 2011; Machado et al., 2015) we explore the use of Machine Learning techniques to create computational models of visual complexity. For that purpose, we use a dataset composed of 800 visual stimuli divided into five categories, describing each stimulus by 329 features based on edge detection, compression error and Zipf's law. In an initial stage, a comparative analysis of representative state-of-the-art Machine Learning approaches is performed. Subsequently, we conduct an exhaustive outlier analysis. We analyze the impact of removing the extreme outliers, concluding that Feature Selection Multiple Kernel Learning obtains the best results, yielding an average correlation to humans' perception of complexity of 0.71 with only twenty-two features. These results outperform the current state-of-the-art, showing the potential of this technique for regression.
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Affiliation(s)
- Carlos Fernandez-Lozano
- Computer Science Department, Faculty of Computer Science, University of A Coruña, A Coruña, Spain
| | - Adrian Carballal
- Computer Science Department, Faculty of Computer Science, University of A Coruña, A Coruña, Spain
| | - Penousal Machado
- CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - Antonino Santos
- Computer Science Department, Faculty of Computer Science, University of A Coruña, A Coruña, Spain
| | - Juan Romero
- Computer Science Department, Faculty of Computer Science, University of A Coruña, A Coruña, Spain
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Bellocchio E, Ciarfuglia TA, Costante G, Valigi P. Weakly Supervised Fruit Counting for Yield Estimation Using Spatial Consistency. IEEE Robot Autom Lett 2019. [DOI: 10.1109/lra.2019.2903260] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Niroui F, Zhang K, Kashino Z, Nejat G. Deep Reinforcement Learning Robot for Search and Rescue Applications: Exploration in Unknown Cluttered Environments. IEEE Robot Autom Lett 2019. [DOI: 10.1109/lra.2019.2891991] [Citation(s) in RCA: 120] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Wang Y, Soetikno B, Furst J, Raicu D, Fawzi AA. Drusen diagnosis comparison between hyper-spectral and color retinal images. BIOMEDICAL OPTICS EXPRESS 2019; 10:914-931. [PMID: 30800523 PMCID: PMC6377880 DOI: 10.1364/boe.10.000914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 01/04/2019] [Accepted: 01/07/2019] [Indexed: 06/09/2023]
Abstract
Age-related macular degeneration (AMD) is a degenerative aging disorder, which can lead to irreversible vision loss in older individuals. The emergence of clinical applications of retinal hyper-spectral imaging provides a unique opportunity to capture important spectral signatures, with the potential to enhance the molecular diagnosis of retinal diseases. In this study, we use a machine learning classification approach to explore whether hyper-spectral images offer an improved outcome compared to standard RGB images. Our results show that the classifier performs better on hyper-spectral images with improved accuracy and sensitivity for drusen classification compared to standard imaging. By examining the most important features in the classification task, our data suggest that drusen are highly heterogeneous. Our work provides further evidence that hyper-spectral retinal image data are uniquely suited for computer-aided diagnosis and detection techniques.
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Affiliation(s)
- Yiyang Wang
- College of Computing and Digital Media, DePaul University, Chicago, Illinois, 60604, USA
| | - Brian Soetikno
- Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Functional Optical Imaging Laboratory, Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA
| | - Jacob Furst
- College of Computing and Digital Media, DePaul University, Chicago, Illinois, 60604, USA
| | - Daniela Raicu
- College of Computing and Digital Media, DePaul University, Chicago, Illinois, 60604, USA
| | - Amani A Fawzi
- Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
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A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8112242] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The requirement for effective image search, which motivates the use of Content-Based Image Retrieval (CBIR) and the search of similar multimedia contents on the basis of user query, remains an open research problem for computer vision applications. The application domains for Bag of Visual Words (BoVW) based image representations are object recognition, image classification and content-based image analysis. Interest point detectors are quantized in the feature space and the final histogram or image signature do not retain any detail about co-occurrences of features in the 2D image space. This spatial information is crucial, as it adversely affects the performance of an image classification-based model. The most notable contribution in this context is Spatial Pyramid Matching (SPM), which captures the absolute spatial distribution of visual words. However, SPM is sensitive to image transformations such as rotation, flipping and translation. When images are not well-aligned, SPM may lose its discriminative power. This paper introduces a novel approach to encoding the relative spatial information for histogram-based representation of the BoVW model. This is established by computing the global geometric relationship between pairs of identical visual words with respect to the centroid of an image. The proposed research is evaluated by using five different datasets. Comprehensive experiments demonstrate the robustness of the proposed image representation as compared to the state-of-the-art methods in terms of precision and recall values.
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