A New Multimedia Web-Data Mining Approach based on Equivalence Class Evaluation Pipelined to Feature Maps onto Planar Projection

Abstract

Multimedia information are semi-organized or unstructured
information elements whose essential substance is separately or by and
large utilized for correspondence. Sight and sound information mining
recognizes, arranges, and recovers important highlights from an assort-
ment of media to recognize enlightening examples furthermore, connec-
tions for information acquisition. Computer Vision (CV)-based systems
have been increasingly popular in recent years, owing to the growing
number and complexity of datasets. In CV, finding meaningful photos
in a huge dataset is a difficult task to solve. Traditional search engines
retrieve photos based on text such as captions and metadata, but this
strategy can result in a lot of irrelevant output, not to speak the time,
effort, and money required to tag this textual data.
In this paper, we proposed a pipelined deep learning oriented method-
ology framework for multimedia web-data mining based on content ex-
tracted feature maps in planner projection as input. Color, texture, form,
and other high-level properties of images are represented as numerical
feature vectors. This technique is based on the following computer vision
tasks in general i.e., Image segmentation, Image classification, Object de-
tection etc. In order to prove the computational efficiency and to validate
its statistical behaviour, we have also presented the experimental eval-
uation on an standard multimedia dataset. The obtained performance
results are then compared with some significant existing approaches in
the terms of various statistical measures/parameters.