Application of Improved Boosting Algorithm for Art Image Classification

In the field of computer science, data mining is a hot topic. It is a mathematical method for identifying patterns in enormous amounts of data. Image mining is an important data mining technique involving a variety of fields. In image mining, art image organization is an interesting research field worthy of attention. The classification of art images into several predetermined sets is referred to as art image categorization. Image preprocessing, feature extraction, object identification, object categorization, object segmentation, object classification, and a variety of other approaches are all part of it. The purpose of this paper is to suggest an improved boosting algorithm that employs a specific method of traditional and simple, yet weak classifiers to create a complex, accurate, and strong classifier image as well as a realistic image. This paper investigated the characteristics of cartoon images, realistic images, painting images, and photo images, created color variance histogram features, and used them for classification. To execute classification experiments, this paper uses an image database of 10471 images, which are randomly distributed into two portions that are used as training data and test data, respectively. The training dataset contains 6971 images, while the test dataset contains 3478 images. The investigational results show that the planned algorithm has a classification accuracy of approximately 97%. The method proposed in this paper can be used as the basis of automatic large-scale image classification and has strong practicability.

Eye Drowsiness Tiredness Detection Based on Driver Experience Using Image Mining

These techniques introduce eye position state and it is parameter as a feasible means of sleepiness recognition. It has been recommended that an increase of eye sleepy state might indicates sleepiness. Thus this method can be used to caution the driver’s risk if driver drives the vehicle. These suggestion were derived from investigative an example of driver’s in attentive and sleepy situation. The gadget evaluate is base on tracking of the eye retina pupil (circular area) to calculate rate of eye sleepy condition. In this research study, individual change in the path of growing sleepiness from a drivers’ eye retina is examined. Data analysis study is interest on the improvement of a prepared display of sleepiness based on an arrangement of eye white and eye black measure values. This will use very accurate operational indicator of drowsiness. However, the main constraint of measure is that driver’s may not show this eye state until they are purely sleepy and/or weaken.

Chromatic-Luminance and True-Texture Analysis for Image Mining in the Social Sciences

This research note highlights the use of raw HSV (Hue, Saturation, Value) colorspace representation (capturing chromatic-luminance), and true-texture (matrix-based) representation of images for image mining applications in the social sciences. Specifically: we focus on the basics of teaching computers to ‘think like people’ in making decisions about what visual content is most interesting or important to human viewers. Our examples capture the facts that, (a) computers see ‘colors as numbers’, rather than as meaningful sections of an image, and (b) computers see texture as ‘numbers’, rather than as meaningful ‘hard’ or ‘soft’ sections of an image. Illustrations are provided using the R packages ‘colorfindr’, ‘glcm’, ‘imager’ and ‘plotly’.