Problem Of Concept Detection Research Articles

Descriptor Optimization for Semantic Concept Detection Using Visual Content

Concept detection has been considered a difficult problem and has attracted the interest of the content-based multimedia retrieval community. This detection implies an association between the concept and the visual content. In other words, the visual characteristics extracted from the video. This includes taking knowledge about the concept itself and its context. This work focuses on the problem of concept detection. For that, several stages are elaborated: first, a method of extraction and semi-automatic annotation of the video plans for the training set is proposed. This new method is based on the genetic algorithm. Then, a preliminary concept detection is carried out to generate the visual dictionary (BoVS). This second step is improved thanks to a noise reduction mechanism. This article's contribution has proven its effectiveness by testing it on a large dataset.

Learning to detect concepts with Approximate Laplacian Eigenmaps in large-scale and online settings

We present a versatile and effective manifold learning approach to tackle the concept detection problem in large-scale and online settings. We demonstrate that Approximate Laplacian Eigenmaps, which constitute a latent representation of the manifold underlying a set of images, offer a compact yet effective feature representation for the problem of concept detection. We expose the theoretical principles of the approach and present an extension that renders the approach applicable in online settings. We evaluate the approach on a number of well-known and two new datasets, coming from the social media domain, and demonstrate that it achieves equal or slightly better detection accuracy compared to supervised methods, while at the same time offering substantial speedup, enabling for instance the training of ten concept detectors using 1.5M images in just 3 min on a commodity server. We also explore a number of factors that affect the detection accuracy of the proposed approach, including the size of training set, the role of unlabelled samples in semi-supervised learning settings, and the performance of the approach across different concepts.

A transductive multi-label learning approach for video concept detection

In this paper, we address two important issues in the video concept detection problem: the insufficiency of labeled videos and the multiple labeling issue. Most existing solutions merely handle the two issues separately. We propose an integrated approach to handle them together, by presenting an effective transductive multi-label classification approach that simultaneously models the labeling consistency between the visually similar videos and the multi-label interdependence for each video. We compare the performance between the proposed approach and several representative transductive and supervised multi-label classification approaches for the video concept detection task over the widely used TRECVID data set. The comparative results demonstrate the superiority of the proposed approach.