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Book ChapterDOI

Edge and Texture Feature Extraction Using Canny and Haralick Textures on SPARK Cluster

D. Sudheer, +2 more
- pp 553-561
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TLDR
A distributed and parallel processing paradigm to accelerate the retrieval process using SPARK stream processing environment is used on the top of the Hadoop Distributed File System (HDFS).
Abstract: 
Image retrieval is the most significant technology forever. Computer vision is improving its trends and methodologies to perform like a human. System can identify any object without a human help by a simple query. To train the system, we need precise algorithms. Content-based image retrieval (CBIR) is the recent emerging trend in computer vision to retrieve relevant images from huge amount of data. This paper used a distributed and parallel processing paradigm to accelerate the retrieval process. SPARK stream processing environment is used on the top of the Hadoop Distributed File System (HDFS). To extract visual content of the images, edge and texture features are used. The distance between query image and database is measured with Mahalanobis distance metric. The performance of the retrieval system has been compared with other distributed image retrieval systems. The proposed methodology using SPARK environment outperforms the existing systems.

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Citations
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Journal ArticleDOI

Experimental evaluation of unsupervised image retrieval application using hybrid feature extraction by integrating deep learning and handcrafted techniques

Sudheer Devulapalli, +3 more
- 03 Jun 2021 - 
TL;DR: A hybrid feature extraction technique by combining the high level features and low level features to improve the robustness of the feature vector is proposed and has achieved the precision of 91 percent which shows better than state of art methods.

Adaptive Feature Engineering Modeling for Ultrasound Image Classification for Decision Support

Hatwib Mugasa
TL;DR: A method of resolving the class imbalance problem with the design of a novel data-adaptive feature engineering model for extracting, selecting, and transforming textural features into a feature space that is inherently relevant to the application domain is proposed.
Journal ArticleDOI

A deep neural architecture for SOTA pneumonia detection from chest X-rays

Sravani Nalluri, +1 more
TL;DR: In this paper , a deep learning model, NASNet (Neural Architecture Search Network), pre-trained on ImageNet, was used to predict pneumonia very early stage through chest x-rays of patients.
Proceedings ArticleDOI

Comparison of Haralick Texture Features and Gray Level Run Length Matrix Features for Analyzing Textural Variation in Cotton Leaves to Identify Spot Disease

P.Purna Chandra Raju, +2 more
TL;DR: In this paper , the textural loss in diseased cotton leaves using Haralick texture features and compare with Gray Level Run Length Matrix (GLRLM) texture features was analyzed.
Proceedings ArticleDOI

Comparison of Haralick Texture Features and Gray Level Run Length Matrix Features for Analyzing Textural Variation in Cotton Leaves to Identify Spot Disease

TL;DR: In this paper , the textural loss in diseased cotton leaves using Haralick texture features and compare with Gray Level Run Length Matrix (GLRLM) texture features is analyzed.
References
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Journal ArticleDOI

Big Data: A Survey

Min Chen, +2 more
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TL;DR: The background and state-of-the-art of big data are reviewed, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid, as well as related technologies.
Journal ArticleDOI

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Verónica Bolón-Canedo, +2 more
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TL;DR: The origins and importance of feature selection are discussed and recent contributions in a range of applications are outlined, from DNA microarray analysis to face recognition.
Journal ArticleDOI

An effective image retrieval scheme using color, texture and shape features

Xiang-Yang Wang, +2 more
- 01 Jan 2011 - 
TL;DR: A new and effective color image retrieval scheme for combining all the three i.e. color, texture and shape information, which achieved higher retrieval efficiency and provides a robust feature set for image retrieval.
Proceedings ArticleDOI

Indexing and searching 100M images with map-reduce

Diana Moise, +3 more
TL;DR: This paper shows how the Map- Reduce paradigm can be applied to indexing algorithms and demonstrates that great scalability can be achieved using Hadoop, a popular Map-Reduce-based framework.
Journal ArticleDOI

A MapReduce-based distributed SVM algorithm for automatic image annotation

Nasullah Khalid Alham, +3 more
- 01 Oct 2011 - 
TL;DR: MRSMO, a MapReduce based distributed SVM algorithm for automatic image annotation that reduces the training time significantly while maintaining a high level of accuracy in both binary and multiclass classifications is presented.