Feature Based Object Mining and Tagging Algorithm for Digital Images

被引：5

作者：

Lad, Hiteshree ^{[1
]}

Mehta, Mayuri A. ^{[1
]}

机构：

[1] Sarvajanik Coll Engn & Technol, Dept Comp Engn, Surat, India

来源：

PROCEEDINGS OF INTERNATIONAL CONFERENCE ON COMMUNICATION AND NETWORKS | 2017年 / 508卷

关键词：

Object recognition; Object mining; Feature extraction; Feature vector; Object tagging;

D O I：

10.1007/978-981-10-2750-5_36

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Object mining is the process of retrieving knowledge about meaningful objects by breaking the image into meaningful components and assigning labels to identified objects. Mining of objects from an image is nontrivial task due to representation of same object using different scales under different viewpoints and illumination changes. Moreover, occlusion and clutter reduce the probability of identification of objects from the image. In this paper, we propose a new Feature based Object Mining and Tagging Algorithm (FOMTA) that decreases the false negative rate. It also increases the probability of identification of objects under occlusion and clutter.

引用

页码：344 / 351

页数：8

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