Research on Various Methods for Visual Concept Detection

Abstract

Visual concept detection is a functional way to detect, manage and classify visual information. No matter it is image retrieval or video retrieval, the system can distinguish object and scene concepts by learning image descriptions itself. However, when the viewing conditions is different or the viewing points is changed, all these will lead to a various result of the image description which collect by the system. So an effective visual concept classification methods should be invariant to different and accidental recording circumstances or conditions. This paper has analysis and summarize the current research status and detection methods of Visual concept detection technologies. Including such methods: salient point detection; social tagged images as a training resource for automated concept detection; extracting distinctive invariant features from images; cross-domain kernel learning method for visual concept detection, etc. And in the end, this paper seeks to unravel the effective when using MIR Flickr in visual concept detection. After comparasion, we want to find the advantages and disadvantages of these typical methods, and hope to give a valuable reference to the following researchers who is interested in visual concept detection and classifications.