Identifying and Analysis of Scene Mining Methods Beased on Scenes Extracted Features

TL;DR

This paper reviews and analyzes various scene mining methods based on features extracted from scenes, proposing a framework to classify, evaluate, and compare these methods amidst diverse scene types and applications.

Contribution

It introduces a comprehensive framework for analyzing scene mining methods based on scene features, aiding comparison and evaluation of different approaches.

Findings

Proposed a framework for scene mining method analysis

Classified scene mining methods based on extracted features

Facilitated comparison of methods across applications

Abstract

Scene mining is a subset of image mining in which scenes are classified to a distinct set of classes based on analysis of their content. In other word in scene mining, a label is given to visual content of scene, for example, mountain, beach. Scene mining is used in applications such as medicine, movie, information retrieval, computer vision, recognition of traffic scene. Reviewing of represented methods shows there are various methods in scene mining. Scene mining applications extension and existence of various scenes, make comparison of methods hard. Scene mining can be followed by identifying scene mining components and representing a framework to analyzing and evaluating methods. In this paper, at first, components of scene mining are introduced, then a framework based on extracted features of scene is represented to classify scene mining methods. Finally, these methods are analyzed and evaluated via a proposal framework.