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 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.

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 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.

An Optimized Method for Image Classification Based on Bag of Words Model

2013 ◽  
Vol 34 (9) ◽  
pp. 2064-2070 ◽  
Author(s):  
Chun-hui Zhao ◽  
Ying Wang ◽  
KANEKO Masahide
Keyword(s):  
Image Classification ◽  
Bag Of Words

An Optimal Framework for Spatial Query Optimization Using Hadoop in Big Data Analytics

2019 ◽  
Vol 12 ◽  
Author(s):  
Pankaj Dadheech ◽  
Dinesh Goyal ◽  
Sumit Srivastava ◽  
Ankit Kumar
Keyword(s):  
Big Data ◽  
Query Optimization ◽  
Spatial Data ◽  
Spatial Information ◽  
Big Data Analytics ◽  
Spatial Query ◽  
Data Process ◽  
Boolean Queries ◽  
Spatial Query Optimization ◽  
Hadoop System

Spatial queries frequently used in Hadoop for significant data process. However, vast and massive size of spatial information makes it difficult to process the spatial inquiries proficiently, so they utilized the Hadoop system for process Big Data. We have used Boolean Queries & Geometry Boolean Spatial Data for Query Optimization using Hadoop System. In this paper, we show a lightweight and adaptable spatial data index for big data which will process in Hadoop frameworks. Results demonstrate the proficiency and adequacy of our spatial ordering system for various spatial inquiries.

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

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