Effective Feature Space Reduction with Imbalanced Data for Semantic Concept Detection (original) (raw)

Correlation-Based Video Semantic Concept Detection Using Multiple Correspondence Analysis

International Symposium on Multimedia, 2008

Semantic concept detection has emerged as an intriguing topic in multimedia research recently. The ability to interpret high-level semantics from low-level features has been the long desired goal of many researchers. In this paper, we propose a novel framework that utilizes the ability of multiple correspondence analysis (MCA) to explore the correlation between different items (feature-value pairs) and classes (concepts) to bridge the gap between the extracted low-level features and high-level semantic concepts. Using the concepts and benchmark data identified and provided by the TRECVID project, we have shown that our proposed framework demonstrates promising results and performs better than the decision tree (DT),support vector machine (SVM), and naive Bayesian (NB) classifiers that are commonly applied to the TRECVID datasets.

TRECVid 2013 Semantic Video Concept Detection by NTT-MD-DUT

2013

In this report, we describe the approaches and experiments on TRECVid 2013 video concept detection conducted by NTT Media Intelligence Laboratories in collaboration with Dalian University of Technology. For this year’s task, we focused our efforts on two aspects. For the first aspect, we investigated the state-of-the-art machine learning algorithm and feature representation for large-scale concept classifiers construction. Specifically, we first evaluated a newly developed powerful image representation which has been successfully adopted in other visual classification task, i.e., Fisher Vector, for concept detection. Meanwhile, we are also interested in the using of deep learning technique for video classification, and to this end, we have tested various settings of deep learning and the results are reported. For the second aspect, we followed the subspace partition based framework we proposed in our last year work and to balance the precision and efficiency, we proposed a sparse so...

Enhancing Multimedia Imbalanced Concept Detection Using VIMP in Random Forests

Proceedings of the ... IEEE International Conference on Information Reuse and Integration. IEEE International Conference on Information Reuse and Integration, 2016

Recent developments in social media and cloud storage lead to an exponential growth in the amount of multimedia data, which increases the complexity of managing, storing, indexing, and retrieving information from such big data. Many current content-based concept detection approaches lag from successfully bridging the semantic gap. To solve this problem, a multi-stage random forest framework is proposed to generate predictor variables based on multivariate regressions using variable importance (VIMP). By fine tuning the forests and significantly reducing the predictor variables, the concept detection scores are evaluated when the concept of interest is rare and imbalanced, i.e., having little collaboration with other high level concepts. Using classical multivariate statistics, estimating the value of one coordinate using other coordinates standardizes the covariates and it depends upon the variance of the correlations instead of the mean. Thus, conditional dependence on the data bei...

Semantic Concept Detection in Videousing MPEG Features

2019

The multimedia storage is increasing day by day. Also the cost to store these multimedia data is very less. Lots of videos available in the video warehouseare in unstructured format. As per user requirement, it is difficult to retrieve the relevant videos from such a huge video storage. Nowadays it is important to make such unstructured multimedia data easily available with flexibility. In recent years, lots of research is going on to extract the semantic concepts from multimedia data.The main purpose of concept detection is to provide semantic concept based retrieval of multimedia content. In previous content-based image and video retrieval systems, the retrieval was based on querying by examples and measuring the similarity of the database objects (images, video shots) with low-level features automatically extracted from the objects. The low-level features are insufficient to explain the content properly at conceptual level.The semantic gap characterizes the difference between two...