scholarly journals Intelligent image classification-based on spatial weighted histograms of concentric circles

2018 ◽  
Vol 15 (3) ◽  
pp. 615-633 ◽  
Author(s):  
Bushra Zafar ◽  
Rehan Ashraf ◽  
Nouman Ali ◽  
Mudassar Ahmed ◽  
Sohail Jabbar ◽  
...  
Keyword(s):  
Image Classification ◽  
Spatial Information ◽  
Dimensional Space ◽  
Research Problem ◽  
Spatial Layout ◽  
Vital Role ◽  
Image Space ◽  
Visual Words ◽  
Extensive Evaluation ◽  
Concentric Circles

As digital images play a vital role in multimedia content, the automatic classification of images is an open research problem. The Bag of Visual Words (BoVW) model is used for image classification, retrieval and object recognition problems. In the BoVW model, a histogram of visual words is computed without considering the spatial layout of the 2-D image space. The performance of BoVW suffers due to a lack of information about spatial details of an image. Spatial Pyramid Matching (SPM) is a popular technique that computes the spatial layout of the 2-D image space. However, SPM is not rotation-invariant and does not allow a change in pose and view point, and it represents the image in a very high dimensional space. In this paper, the spatial contents of an image are added and the rotations are dealt with efficiently, as compared to approaches that incorporate spatial contents. The spatial information is added by constructing the histogram of circles, while rotations are dealt with by using concentric circles. A weighed scheme is applied to represent the image in the form of a histogram of visual words. Extensive evaluation of benchmark datasets and the comparison with recent classification models demonstrate the effectiveness of the proposed approach. The proposed representation outperforms the state-of-the-art methods in terms of classification accuracy.

scholarly journals A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR

2018 ◽  
Vol 8 (11) ◽  
pp. 2242 ◽  
Author(s):  
Bushra Zafar ◽  
Rehan Ashraf ◽  
Nouman Ali ◽  
Muhammad Iqbal ◽  
Muhammad Sajid ◽  
...  
Keyword(s):  
Image Classification ◽  
Spatial Information ◽  
Image Representation ◽  
Feature Space ◽  
Research Problem ◽  
Visual Words ◽  
Spatial Image ◽  
User Query ◽  
Novel Approach ◽  
Image Representations

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.

scholarly journals Mining Regional Co-Occurrence Patterns for Image Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Zhihang Ji ◽  
Sining Wu ◽  
Fan Wang ◽  
Lijuan Xu ◽  
Yan Yang ◽  
...  
Keyword(s):  
Image Classification ◽  
Large Scale ◽  
Spatial Information ◽  
Multiple Kernel Learning ◽  
The Novel ◽  
Color Information ◽  
Local Color ◽  
Structure Information ◽  
Visual Words ◽  
Occurrence Patterns

In the context of image classification, bag-of-visual-words mode is widely used for image representation. In recent years several works have aimed at exploiting color or spatial information to improve the representation. In this paper two kinds of representation vectors, namely, Global Color Co-occurrence Vector (GCCV) and Local Color Co-occurrence Vector (LCCV), are proposed. Both of them make use of the color and co-occurrence information of the superpixels in an image. GCCV describes the global statistical distribution of the colorful superpixels with embedding the spatial information between them. By this way, it is capable of capturing the color and structure information in large scale. Unlike the GCCV, LCCV, which is embedded in the Riemannian manifold space, reflects the color information within the superpixels in detail. It records the higher-order distribution of the color between the superpixels within a neighborhood by aggregating the co-occurrence information in the second-order pooling way. In the experiment, we incorporate the two proposed representation vectors with feature vector like LLC or CNN by Multiple Kernel Learning (MKL) technology. Several challenging datasets for visual classification are tested on the novel framework, and experimental results demonstrate the effectiveness of the proposed method.

scholarly journals Integrated SURF and Spatial Augmented Color Feature Based Bovw Model with Svm for Image Classification

2019 ◽  
Vol 8 (6) ◽  
pp. 1875-1877
Keyword(s):  
Image Classification ◽  
Spatial Information ◽  
Feature Space ◽  
Feature Reduction ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Local Color ◽  
Visual Words ◽  
Speed Up ◽  
Multiclass Svm

In this paper, Bag-of-visual-words (BoVW) model with Speed up robust features (SURF) and spatial augmented color features for image classification is proposed. In BOVW model image is designated as vector of features occurrence count. This model ignores spatial information amongst patches, and SURF Feature descriptor is relevant to gray images only. As spatial layout of the extracted feature is important and color is a vital feature for image recognition, in this paper local color layout feature is augmented with SURF feature. Feature space is quantized using K-means clustering for feature reduction in constructing visual vocabulary. Histogram of visual word occurrence is then obtained which is applied to multiclass SVM classifier. Experimental results show that accuracy is improved with the proposed method.

scholarly journals Spectral–Spatial Discriminant Feature Learning for Hyperspectral Image Classification

2019 ◽  
Vol 11 (13) ◽  
pp. 1552 ◽  
Author(s):  
Dong ◽  
Naghedolfeizi ◽  
Aberra ◽  
Zeng
Keyword(s):  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Dimensional Space ◽  
Feature Learning ◽  
Laplacian Matrix ◽  
High Dimensional ◽  
Spectral Structure ◽  
Hyperspectral Image Classification ◽  
Hypergraph Laplacian

Sparse representation classification (SRC) is being widely applied to target detection in hyperspectral images (HSI). However, due to the problem in HSI that high-dimensional data contain redundant information, SRC methods may fail to achieve high classification performance, even with a large number of spectral bands. Selecting a subset of predictive features in a high-dimensional space is an important and challenging problem for hyperspectral image classification. In this paper, we propose a novel discriminant feature learning (DFL) method, which combines spectral and spatial information into a hypergraph Laplacian. First, a subset of discriminative features is selected, which preserve the spectral structure of data and the inter- and intra-class constraints on labeled training samples. A feature evaluator is obtained by semi-supervised learning with the hypergraph Laplacian. Secondly, the selected features are mapped into a further lower-dimensional eigenspace through a generalized eigendecomposition of the Laplacian matrix. The finally extracted discriminative features are used in a joint sparsity-model algorithm. Experiments conducted with benchmark data sets and different experimental settings show that our proposed method increases classification accuracy and outperforms the state-of-the-art HSI classification methods.

scholarly journals A Novel Image Retrieval Based on a Combination of Local and Global Histograms of Visual Words

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Zahid Mehmood ◽  
Syed Muhammad Anwar ◽  
Nouman Ali ◽  
Hafiz Adnan Habib ◽  
Muhammad Rashid
Keyword(s):  
Image Retrieval ◽  
Spatial Information ◽  
Image Representation ◽  
Ground Truth ◽  
Spatial Layout ◽  
Bag Of Visual Words ◽  
Significant Information ◽  
Visual Words ◽  
Ground Truth Image ◽  
Similar Images

Content-based image retrieval (CBIR) provides a sustainable solution to retrieve similar images from an image archive. In the last few years, the Bag-of-Visual-Words (BoVW) model gained attention and significantly improved the performance of image retrieval. In the standard BoVW model, an image is represented as an orderless global histogram of visual words by ignoring the spatial layout. The spatial layout of an image carries significant information that can enhance the performance of CBIR. In this paper, we are presenting a novel image representation that is based on a combination of local and global histograms of visual words. The global histogram of visual words is constructed over the whole image, while the local histogram of visual words is constructed over the local rectangular region of the image. The local histogram contains the spatial information about the salient objects. Extensive experiments and comparisons conducted on Corel-A, Caltech-256, and Ground Truth image datasets demonstrate that the proposed image representation increases the performance of image retrieval.

Navigating in a volumetric world: Metric encoding in the vertical axis of space

2013 ◽  
Vol 36 (5) ◽  
pp. 546-547 ◽  
Author(s):  
Theresa Burt de Perera ◽  
Robert Holbrook ◽  
Victoria Davis ◽  
Alex Kacelnik ◽  
Tim Guilford
Keyword(s):  
Spatial Information ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Vertical Component ◽  
Vertical Axis ◽  
Orthogonal Axis ◽  
Experimental Protocols ◽  
Three Dimensional Space ◽  
The Way

AbstractAnimals navigate through three-dimensional environments, but we argue that the way they encode three-dimensional spatial information is shaped by how they use the vertical component of space. We agree with Jeffery et al. that the representation of three-dimensional space in vertebrates is probably bicoded (with separation of the plane of locomotion and its orthogonal axis), but we believe that their suggestion that the vertical axis is stored “contextually” (that is, not containing distance or direction metrics usable for novel computations) is unlikely, and as yet unsupported. We describe potential experimental protocols that could clarify these differences in opinion empirically.

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