Abstract
One of the most important applications of data science and data mining is is organizing, classifying, and retrieving digital images on Internet. The current focus of the researchers is to develop methods for the content based exploration of natural scenery images. In this research paper, a self-organizing method of natural scenes images using Wiener-Granger Causality theory is proposed. It is achieved by carrying out Wiener-Granger causality for organizing the features in the time series form and introducing a characteristics extraction stage at random points within the image. Once the causal relationships are obtained, the k-means algorithm is applied to achieve the self-organizing of these attributes. Regarding classification, the k−NN distance classification algorithm is used to find the most similar images that share the causal relationships between the elements of the scenes. The proposed methodology is validated on three public image databases, obtaining 100% recovery results.
Highlights
One of the most important applications of data science and data mining is is organizing, classifying, and retrieving digital images on Internet
F Computer-aided diagnosis of mammography masses based on a supervised content-based image retrieval approach [4]
This research paper proposes the utilization of the Wiener-Granger causality theory, together with the content-based image retrieval (CBIR) self-organization analysis
Summary
One of the most important applications of data science and data mining is is organizing, classifying, and retrieving digital images on Internet. A self-organizing method of natural scenes images using Wiener-Granger Causality theory is proposed. It is achieved by carrying out Wiener-Granger causality for organizing the features in the time series form and introducing a characteristics extraction stage at random points within the image. With the increasing usage of internet and digital gadgets, content-based image retrieval (CBIR) has grown and been applied in fields such as artificial vision and artificial intelligence [1]. Since any object or scene cannot be recognized efficiently by a single algorithm, this leaves the door open for computer vision applications where areas such as digital image processing, pattern recognition, machine learning, etc. A CBIR system is necessary, which would help the users to find relevant images having self-contained features based on our visual perception
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