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.

A Combination of Spatial Pyramid and Inverted Index for Large-Scale Image Retrieval

2018 ◽  
pp. 1307-1321
Author(s):  
Vinh-Tiep Nguyen ◽  
Thanh Duc Ngo ◽  
Minh-Triet Tran ◽  
Duy-Dinh Le ◽  
Duc Anh Duong
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Spatial Information ◽  
Real Life ◽  
Inverted Index ◽  
Bag Of Words ◽  
Visual Words ◽  
Benchmark Datasets ◽  
Large Scale Image Retrieval ◽  
Inverted Indexing

Large-scale image retrieval has been shown remarkable potential in real-life applications. The standard approach is based on Inverted Indexing, given images are represented using Bag-of-Words model. However, one major limitation of both Inverted Index and Bag-of-Words presentation is that they ignore spatial information of visual words in image presentation and comparison. As a result, retrieval accuracy is decreased. In this paper, the authors investigate an approach to integrate spatial information into Inverted Index to improve accuracy while maintaining short retrieval time. Experiments conducted on several benchmark datasets (Oxford Building 5K, Oxford Building 5K+100K and Paris 6K) demonstrate the effectiveness of our proposed approach.

A Combination of Spatial Pyramid and Inverted Index for Large-Scale Image Retrieval

2015 ◽  
Vol 6 (2) ◽  
pp. 37-51 ◽  
Author(s):  
Vinh-Tiep Nguyen ◽  
Thanh Duc Ngo ◽  
Minh-Triet Tran ◽  
Duy-Dinh Le ◽  
Duc Anh Duong
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Spatial Information ◽  
Real Life ◽  
Inverted Index ◽  
Bag Of Words ◽  
Visual Words ◽  
Benchmark Datasets ◽  
Large Scale Image Retrieval ◽  
Inverted Indexing

Large-scale image retrieval has been shown remarkable potential in real-life applications. The standard approach is based on Inverted Indexing, given images are represented using Bag-of-Words model. However, one major limitation of both Inverted Index and Bag-of-Words presentation is that they ignore spatial information of visual words in image presentation and comparison. As a result, retrieval accuracy is decreased. In this paper, the authors investigate an approach to integrate spatial information into Inverted Index to improve accuracy while maintaining short retrieval time. Experiments conducted on several benchmark datasets (Oxford Building 5K, Oxford Building 5K+100K and Paris 6K) demonstrate the effectiveness of our proposed approach.

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.

Information System in Image Classification Based on SVM and Color Clustering Analysis

2014 ◽  
Vol 886 ◽  
pp. 572-575
Author(s):  
Li Ying Qi ◽  
Ke Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Spatial Information ◽  
Basic Research ◽  
Color Histogram ◽  
Support Vector ◽  
Color Information ◽  
Machine Model ◽  
Color Feature ◽  
Image Domain

Effective use of the color feature of Content Based Image Retrieval (CBIR) and Image classification is an important basic research, but there are some shortcomings in the color histogram representation method, such as high dimension, pixels spatial information is ignored and so on. Although color feature data can reduce the dimension by quantification, but some useful image color information will be discard. In this paper, the image color information processing in space constrained fuzzy clustering to obtain a lower dimensional color feature data of the image characteristics of domain colors description, and use multi-class support vector machine to classify color images. Experimental results show that the proposed method can better describe image color information than color histogram; image domain color description combined with support vector machine model can achieve the automatic classification of images effectively.

STRUCTURALLY ENHANCED INCREMENTAL NEURAL LEARNING FOR IMAGE CLASSIFICATION WITH SUBGRAPH EXTRACTION

2014 ◽  
Vol 24 (07) ◽  
pp. 1450024 ◽  
Author(s):  
YU-BIN YANG ◽  
YA-NAN LI ◽  
YANG GAO ◽  
HUJUN YIN ◽  
YE TANG
Keyword(s):  
Image Classification ◽  
Large Scale ◽  
Structural Information ◽  
Computational Cost ◽  
Image Features ◽  
Graph Representation ◽  
Visual Words ◽  
Neural Learning ◽  
Learning Technique ◽  
Codebook Learning

In this paper, a structurally enhanced incremental neural learning technique is proposed to learn a discriminative codebook representation of images for effective image classification applications. In order to accommodate the relationships such as structures and distributions among visual words into the codebook learning process, we develop an online codebook graph learning method based on a novel structurally enhanced incremental learning technique, called as "visualization-induced self-organized incremental neural network (ViSOINN)". The hidden structural information in the images is embedded into the graph representation evolving dynamically with the adaptive and competitive learning mechanism. Afterwards, image features can be coded using a sub-graph extraction process based on the learned codebook graph, and a classifier is subsequently used to complete the image classification task. Compared with other codebook learning algorithms originated from the classical Bag-of-Features (BoF) model, ViSOINN holds the following advantages: (1) it learns codebook efficiently and effectively from a small training set; (2) it models the relationships among visual words in metric scaling fashion, so preserving high discriminative power; (3) it automatically learns the codebook without a fixed pre-defined size; and (4) it enhances and preserves better the structure of the data. These characteristics help to improve image classification performance and make it more suitable for handling large-scale image classification tasks. Experimental results on the widely used Caltech-101 and Caltech-256 benchmark datasets demonstrate that ViSOINN achieves markedly improved performance and reduces the computational cost considerably.

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.

The Shop-Girl Revolutionary in Henry James's The Princess Casamassima

2008 ◽  
Vol 63 (2) ◽  
pp. 197-222 ◽  
Author(s):  
Katherine Mullin
Keyword(s):  
Popular Culture ◽  
Social Change ◽  
The Sun ◽  
The Novel ◽  
Local Color ◽  
Literary Naturalism ◽  
Public House ◽  
Revolutionary Politics ◽  
Critical Attention ◽  
Marginal Characters

Abstract This essay argues that the complex political resonances of Henry James's The Princess Casamassima (1886) can be further elucidated through closer critical attention to one of its more marginal characters, the shop-girl Millicent Henning. Ebullient, assertive, and, for many early reviewers, the novel's sole redeeming feature, Millicent supplies the novel with far more than local color. Instead, James seizes on a sexual persona already well established within literary naturalism and popular culture alike to explore a rival mode of insurrection to that more obviously offered elsewhere. While the modes of revolution contemplated by Hyacinth Robinson and his comrades in the Sun and Moon public house are revealed to be anachronistic and ineffectual, Millicent's canny manipulation of her sexuality supplies her with an alternative, effective, and unmistakably modern mode of transformation. The novel's portrait of ““revolutionary politics of a hole-and-corner sort”” is thus set against Millicent's brand of quotidian yet inexorable social change.

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