RETRACTED ARTICLE: The efficient fast-response content-based image retrieval using spark and MapReduce model framework

Hussain, D. Mansoor; Surendran, D.

doi:10.1007/s12652-020-01775-9

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…According to Hussain and Surendran [86], efficient content-based fast-response picture retrieval is explored using the MapReduce and Spark model framework. The authors leverage the MapReduce model structure to sign efficiently and index massive volumes of photos, enabling fast retrieval based on content.…”

Section: Hadoop Vs Sparkmentioning

confidence: 99%

The scheduling techniques in the Hadoop and Spark of smart cities environment: a systematic review

Mirza,

Ali,

Ishak

2024

Bulletin EEI

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Processing extensive and diverse data in real-time is a significant challenge in the context of smart cities. Timely access to information and efficient analytics is essential for smart city services to make data-driven decisions and enhance urban living. Scheduling algorithms play a crucial role in ensuring the prompt delivery of services and efficient task completion. This paper explores various scheduling techniques, including static, dynamic, and hybrid schedulers, and compares their objectives and performance. Additionally, the study examines two prominent data processing frameworks, Hadoop and Spark, and compares their capabilities in handling big data in smart cities. With its ability to process large amounts of data quickly and efficiently, Spark has shown superiority over Hadoop in real-time data processing and performance optimization. The paper concludes by highlighting the strengths and limitations of each framework. It discusses the need for further research in optimizing scheduling techniques and exploring hybrid artificial intelligence scheduling for Spark. Overall, the findings contribute to a better understanding of data processing in real-time and provide insights for researchers and practitioners in smart cities.

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Section: Hadoop Vs Sparkmentioning

confidence: 99%

The scheduling techniques in the Hadoop and Spark of smart cities environment: a systematic review

Mirza,

Ali,

Ishak

2024

Bulletin EEI

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“…These vectors allow different features to be assembled into a compact vector [13]. Principal component analysis (PCA) also allows the dimension of the feature vector to be reduced [2,7,21]. The authors in [22] studied the different reductions related to PCA applied to scale-invariant feature transform (SIFT) and Speeded Up Robust Features (SURF) descriptors on Wang (http://wang.ist.psu.edu/docs/ related/, accessed on 20 March 2021) and Coil100 (https://www1.cs.columbia.edu/CAVE/ software/softlib/coil-100.php, accessed on 20 March 2021) databases and concluded that the optimal reduction was at 70%.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

“…The performance of a CBIR can be measured within precision [2,6] and recall [7], and according to different authors, these are the most widely used methods [8,11,34]. A high precision value implies that the system returns more relevant images than irrelevant ones, while a high recall value means that the system returns most of the relevant images [7].…”

Section: Performance Measuresmentioning

confidence: 99%

“…Other images can be present in different domains (medicine, spatial) which need to be found quickly and efficiently [1]. This is becoming an important area of research, which is called image retrieval [2]. This field of research is useful for different sectors such as video surveillance [3], for example, to identify cars where a faster search is desired with high accuracy, identification of people [3,4]; e-commerce, to identify shopping recommendations based on photos; various applications to recognize flowers, mushrooms, etc.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Reduction Methods Applied on a Convolutional Neural Network

2022

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With the emergence of smartphones, video surveillance cameras, social networks, and multimedia engines, as well as the development of the internet and connected objects (the Internet of Things—IoT), the number of available images is increasing very quickly. This leads to the necessity of managing a huge amount of data using Big Data technologies. In this context, several sectors, such as security and medicine, need to extract image features (index) in order to quickly and efficiently find these data with high precision. To reach this first goal, two main approaches exist in the literature. The first one uses classical methods based on the extraction of visual features, such as color, texture, and shape for indexation. The accuracy of these methods was acceptable until the early 2010s. The second approach is based on convolutional neuronal networks (CNN), which offer better precision due to the largeness of the descriptors, but they can cause an increase in research time and storage space. To decrease the research time, one needs to reduce the size of these vectors (descriptors) by using dimensionality reduction methods. In this paper, we propose an approach that allows the problem of the “curse of dimensionality” to be solved thanks to an efficient combination of convolutional neural networks and dimensionality reduction methods. Our contribution consists of defining the best combination approach between the CNN layers and the regional maximum activation of convolutions (RMAC) method and its variants. With our combined approach, we propose providing reduced descriptors that will accelerate the research time and reduce the storage space while maintaining precision. We conclude by proposing the best position of an RMAC layer with an increase in accuracy ranging from 4.03% to 27.34%, a decrease in research time ranging from 89.66% to 98.14% in the function of CNN architecture, and a reduction in the size of the descriptor vector by 97.96% on the GHIM-10K benchmark database.

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“…Hussain et al [47] proposed a MapReduced and Spark-based model system for quick image indexing and retrieval. They further integrated the K-Nearest Neighbors classifier for image retrieval.…”

Section: Literature Reviewmentioning

confidence: 99%

Integration of Discrete Wavelet Transform, DBSCAN, and Classifiers for Efficient Content Based Image Retrieval

et al. 2020

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In the domain of computer vision, the efficient representation of an image feature vector for the retrieval of images remains a significant problem. Extensive research has been undertaken on Content-Based Image Retrieval (CBIR) using various descriptors, and machine learning algorithms with certain descriptors have significantly improved the performance of these systems. In this proposed research, a new scheme for CBIR was implemented to address the semantic gap issue and to form an efficient feature vector. This technique was based on the histogram formation of query and dataset images. The auto-correlogram of the images was computed w.r.t RGB format, followed by a moment’s extraction. To form efficient feature vectors, Discrete Wavelet Transform (DWT) in a multi-resolution framework was applied. A codebook was formed using a density-based clustering approach known as Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The similarity index was computed using the Euclidean distance between the feature vector of the query image and the dataset images. Different classifiers, like Support Vector (SVM), K-Nearest Neighbor (KNN), and Decision Tree, were used for the classification of images. The set experiment was performed on three publicly available datasets, and the performance of the proposed framework was compared with another state of the proposed frameworks which have had a positive performance in terms of accuracy.

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RETRACTED ARTICLE: The efficient fast-response content-based image retrieval using spark and MapReduce model framework

Cited by 12 publications

References 14 publications

The scheduling techniques in the Hadoop and Spark of smart cities environment: a systematic review

The scheduling techniques in the Hadoop and Spark of smart cities environment: a systematic review

A Comparative Study of Reduction Methods Applied on a Convolutional Neural Network

Integration of Discrete Wavelet Transform, DBSCAN, and Classifiers for Efficient Content Based Image Retrieval

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