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 Application of Analytics in Machine Vision using Big Data

2019 ◽  
Vol 7 (4) ◽  
Author(s):  
Noha Elfiky
Keyword(s):  
Big Data ◽  
Spatial Structure ◽  
Image Classification ◽  
Large Scale ◽  
Spatial Information ◽  
Image Representation ◽  
Structural Description ◽  
Bag Of Words ◽  
Spatial Image ◽  
3D Scene

The Bag-of-Words (BoW) approach has been successfully applied in the context of category-level image classification. To incorporate spatial image information in the BoW model, Spatial Pyramids (SPs) are used. However, spatial pyramids are rigid in nature and are based on pre-defined grid configurations. As a consequence, they often fail to coincide with the underlying spatial structure of images from different categories which may negatively affect the classification accuracy.The aim of the paper is to use the 3D scene geometry to steer the layout of spatial pyramids for category-level image classification (object recognition). The proposed approach provides an image representation by inferring the constituent geometrical parts of a scene. As a result, the image representation retains the descriptive spatial information to yield a structural description of the image. From large scale experiments on the Pascal VOC2007 and Caltech101, it can be derived that SPs which are obtained by the proposed Generic SPs outperforms the standard SPs.

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 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 Spectral-Spatial Hyperspectral Image Classification Based on Homogeneous Minimum Spanning Forest

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
F. Poorahangaryan ◽  
H. Ghassemian
Keyword(s):  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Feature Space ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Spatial Classification ◽  
Spanning Forest ◽  
Minimum Spanning Forest ◽  
Initial Classification

The combination of spectral and spatial information is known as a suitable way to improve the accuracy of hyperspectral image classification. In this paper, we propose a spectral-spatial hyperspectral image classification approach composed of the following stages. Initially, the support vector machine (SVM) is applied to obtain the initial classification map. Then, we present a new index called the homogeneity order and, using that with K-nearest neighbors, we select some pixels in feature space. The extracted pixels are considered as markers for Minimum Spanning Forest (MSF) construction. The class assignment to the markers is done using the initial classification map results. In the final stage, MSF is applied to these markers, and a spectral-spatial classification map is obtained. Experiments performed on several real hyperspectral images demonstrate that the classification accuracies obtained by the proposed scheme are improved when compared to MSF-based spectral-spatial classification approaches.

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.

A STUDY ON COMBINING IMAGE REPRESENTATIONS FOR IMAGE CLASSIFICATION AND RETRIEVAL

2004 ◽  
Vol 18 (05) ◽  
pp. 867-890 ◽  
Author(s):  
CARMEN LAI ◽  
DAVID M. J. TAX ◽  
ROBERT P. W. DUIN ◽  
ELŻBIETA PĘKALSKA ◽  
PAVEL PACLÍK
Keyword(s):  
Image Classification ◽  
Feature Space ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Retrieval Performance ◽  
Mahalanobis Distances ◽  
Similar Images ◽  
Image Representations ◽  
Cloud Of Points

A flexible description of images is offered by a cloud of points in a feature space. In the context of image retrieval such clouds can be represented in a number of ways. Two approaches are here considered. The first approach is based on the assumption of a normal distribution, hence homogeneous clouds, while the second one focuses on the boundary description, which is more suitable for multimodal clouds. The images are then compared either by using the Mahalanobis distance or by the support vector data description (SVDD), respectively. The paper investigates some possibilities of combining the image clouds based on the idea that responses of several cloud descriptions may convey a pattern, specific for semantically similar images. A ranking of image dissimilarities is used as a comparison for two image databases targeting image classification and retrieval problems. We show that combining of the SVDD descriptions improves the retrieval performance with respect to ranking, on the contrary to the Mahalanobis case. Surprisingly, it turns out that the ranking of the Mahalanobis distances works well also for inhomogeneous images.

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.

scholarly journals MULTI-FEATURE MUTUAL INFORMATION IMAGE REGISTRATION

2012 ◽  
Vol 31 (1) ◽  
pp. 43 ◽  
Author(s):  
Dejan Tomaževič ◽  
Boštjan Likar ◽  
Franjo Pernuš
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Spatial Information ◽  
Similarity Measures ◽  
Feature Space ◽  
Efficient Estimation ◽  
Mr Images ◽  
Feature Correspondence ◽  
Information Theoretic ◽  
Novel Approach

Nowadays, information-theoretic similarity measures, especially the mutual information and its derivatives, are one of the most frequently used measures of global intensity feature correspondence in image registration. Because the traditional mutual information similarity measure ignores the dependency of intensity values of neighboring image elements, registration based on mutual information is not robust in cases of low global intensity correspondence. Robustness can be improved by adding spatial information in the form of local intensity changes to the global intensity correspondence. This paper presents a novel method, by which intensities, together with spatial information, i.e., relations between neighboring image elements in the form of intensity gradients, are included in information-theoretic similarity measures. In contrast to a number of heuristic methods that include additional features into the generic mutual information measure, the proposed method strictly follows information theory under certain assumptions on feature probability distribution. The novel approach solves the problem of efficient estimation of multifeature mutual information from sparse high-dimensional feature space. The proposed measure was tested on magnetic resonance (MR) and computed tomography (CT) images. In addition, the measure was tested on positron emission tomography (PET) and MR images from the widely used Retrospective Image Registration Evaluation project image database. The results indicate that multi-feature mutual information, which combines image intensities and intensity gradients, is more robust than the standard single-feature intensity based mutual information, especially in cases of low global intensity correspondences, such as in PET/MR images or significant intensity inhomogeneity.

scholarly journals Experiments of Image Classification Using Dissimilarity Spaces Built with Siamese Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1573
Author(s):  
Loris Nanni ◽  
Giovanni Minchio ◽  
Sheryl Brahnam ◽  
Gianluca Maguolo ◽  
Alessandra Lumini
Keyword(s):  
Vector Space ◽  
Image Classification ◽  
Ad Hoc ◽  
Feature Space ◽  
Medical Data ◽  
Training Data ◽  
Data Sets ◽  
Large Set ◽  
Clustering Methods ◽  
Siamese Networks

Traditionally, classifiers are trained to predict patterns within a feature space. The image classification system presented here trains classifiers to predict patterns within a vector space by combining the dissimilarity spaces generated by a large set of Siamese Neural Networks (SNNs). A set of centroids from the patterns in the training data sets is calculated with supervised k-means clustering. The centroids are used to generate the dissimilarity space via the Siamese networks. The vector space descriptors are extracted by projecting patterns onto the similarity spaces, and SVMs classify an image by its dissimilarity vector. The versatility of the proposed approach in image classification is demonstrated by evaluating the system on different types of images across two domains: two medical data sets and two animal audio data sets with vocalizations represented as images (spectrograms). Results show that the proposed system’s performance competes competitively against the best-performing methods in the literature, obtaining state-of-the-art performance on one of the medical data sets, and does so without ad-hoc optimization of the clustering methods on the tested data sets.

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