PCA as Dimensionality Reduction for Large-Scale Image Retrieval Systems

Belarbi, Mohammed Amin; Mahmoudi, Saïd; Belalem, Ghalem

doi:10.4018/ijaci.2017100104

Cited by 41 publications

(33 citation statements)

References 28 publications

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“…One technique to reduce the number of attributes is dimensionality reduction [14]. One example algorithm for dimensionality reduction is principal component analysis (PCA) [15], which is an expensive process. Most machine learning algorithms require multiple scans of the dataset with time complexities based on the number of data records, therefore, reducing the number of data records seems to be the way to go for big data analysis.…”

Section: Techniques For Data Reductionmentioning

confidence: 99%

Bayes Classification and Entropy Discretization of Large Datasets using Multi-Resolution Data Aggregation

Alwajidi¹,

Yang²

2020

Adv. sci. technol. eng. syst. j.

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Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such as decision tree and nearest neighbor search. The proposed method can handle streaming data efficiently and, for entropy discretization, provide sufficient information to find the optimal split variable and the optimal split value.

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Section: Techniques For Data Reductionmentioning

confidence: 99%

Bayes Classification and Entropy Discretization of Large Datasets using Multi-Resolution Data Aggregation

Alwajidi¹,

Yang²

2020

Adv. sci. technol. eng. syst. j.

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“…The next step consisted of comparing the query image features with those of image database. The comparison was performed within two similarity matching methods: Flann based matcher and Brute force matcher [7]. Our selection of these two methods was due to their efficiency and fast execution, which is so important in our case.…”

Section: Researchmentioning

confidence: 99%

“…Therefore, we propose a cloud-based platform that groups and integrates image and video processing algorithms, which are exploited and combined for providing an efficient method of image indexation and retrieval. The proposed combination of descriptors is well adapted for dimensionality reduction, where the selection of the most significant values of descriptors (using PCA method) allowed to reduce the research time with the maintain of precision [7]. As a result, our method is well suited for large scale image retrieval.…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based Image Retrieval Using GPU Platforms

et al. 2019

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The process of image retrieval presents an interesting tool for different domains related to computer vision such as multimedia retrieval, pattern recognition, medical imaging, video surveillance and movements analysis. Visual characteristics of images such as color, texture and shape are used to identify the content of images. However, the retrieving process becomes very challenging due to the hard management of large databases in terms of storage, computation complexity, temporal performance and similarity representation. In this paper, we propose a cloud-based platform in which we integrate several features extraction algorithms used for content-based image retrieval (CBIR) systems. Moreover, we propose an efficient combination of SIFT and SURF descriptors that allowed to extract and match image features and hence improve the process of image retrieval. The proposed algorithms have been implemented on the CPU and also adapted to fully exploit the power of GPUs. Our platform is presented with a responsive web solution that offers for users the possibility to exploit, test and evaluate image retrieval methods. The platform offers to users a simple-to-use access for different algorithms such as SIFT, SURF descriptors without the need to setup the environment or install anything while spending minimal efforts on preprocessing and configuring. On the other hand, our cloud-based CPU and GPU implementations are scalable, which means that they can be used even with large database of multimedia documents. The obtained results showed: 1. Precision improvement in terms of recall and precision; 2. Performance improvement in terms of computation time as a result of exploiting GPUs in parallel; 3. Reduction of energy consumption.

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“…Dimensionality reduction is a process of converting data from a high dimensional space to a lower dimensional space with the aim of preserving meaningful information from the original data. Dimensionality reduction can be applied in any field that has high dimensional data (a large number of variables) such as signal processing [1], speech recognition [2,3], neuroinformatics [4,5], bioinformatics [6,7], social media [8,9], telecoms [10], and computer vision [11], for data visualization, data exploration, noise reduction or as a pre-processing step to support classification models. An appropriate dimensionality reduction technique is related to the goodness of preserving the geometry (structure) of the data of interest.…”

Section: Introductionmentioning

confidence: 99%