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Integrating Data Mining and Image-Based Information Systems: Trends and Innovations

Jun

16

2025

Integrating Data Mining and Image-Based Information Systems: Trends and Innovations

Each individual pixel position of an image under analysis serves as the focal point of a square-block query that yields an estimated class label. Image-driven data mining techniques for image data segmentation, classification, and attribution. If there are enough attribute strata in the information warehouse, feature attribute estimates can also be mined. New techniques for attribute mining, class label assignments, comparable pattern scoring, and pixel-block mining are introduced. The ability of image mining to automatically extract valuable information from vast amounts of image data is becoming more and more popular. It is a multidisciplinary endeavor that basically depends on knowledge of computer vision, artificial intelligence, databases, data mining, digital image processing, content-based image recovery, and machine learning. Content-driven image retrieval is a common approach for retrieving comparable images based on query features. Image mining is the process of extracting valuable information from a large dataset of images.

Search engines are becoming ubiquitous, providing access to a massive amount of material available on the internet. Because web users often focus on the first pages of the inquiry results, scoring methods are begun, resembling a bias for seeing the web. Retrieval of images with text has been practiced for a while, since image retrieval is on the agenda of every major crop. As a result, it is believed that data mining techniques, combined with image color analysis and retrieval based on content from images, might provide a more effective process for feature extraction, as well as the prediction of nearest neighbor and estimation algorithms, to build the system being considered. Relevant comments can also help attain better performance. Third, the procedures for looking for intriguing photographs could potentially be considered as transaction records and converted into usable data. The system can determine the users' intents for accessing images via data mining techniques. The found guidelines might be utilized to increase retrieval efficiency. Because of the advancements in technology, it is now possible for even non-professional users to record imagery. Every day, a great number of image, audio, and video data sets are uploaded by various user communities throughout the world. Extracting the necessary information from such a large amount of stuff is a difficult task. Image extraction is done using either a text-based query or an image attribute-based retrieval.

In this special issue, an image-based traffic monitoring strategy. This method is an important part of an automated traffic information providing system. It turns out that the image histograms fluctuate depending on pedestrian circumstances. Common properties of histograms are identified using a machine-learning algorithm.

Possible topics include, but are not limited to:

Image-based data mining for information segmentation, sorting, and attribution
Architectural Knowledge and Appraisal for Image-Mining-Based Decision Assistance Systems
A specialized system: image categorization using data mining methodologies
An evaluation of content-driven image retrieval systems for image mining
Related to information image extraction using data mining approaches
Identifying intriguing images with image categorization a fuzzy model
Research analyses on sensing image classification utilizing data mining
Image-based stochastic spinning to preserve data throughout the data mining process
Image pixle analysis approaches for retrieving multimedia data through data mining
A Data Mining-Specific Query for Image Extraction of User Interest
Heterogeneous data extraction for Biomedical Image and Media Data Mining