Feature Selection Using Correlation and Reliability Based Scoring Metric for Video Semantic Detection

Abstract

Abstract-Content-based multimedia retrieval faces many challenges such as semantic gap, imbalanced data, and varied qualities of the media. Feature selection as a component of the retrieval process plays an important role. The aim of feature selection is to identify a subset of features by removing irrelevant or redundant features. An effective subset of features can not only improve model performance and reduce computational complexity, but also enhance semantic interpretability. To achieve these objectives, in this paper, a novel metric that integrates the correlation and reliability information between each feature and each class obtained from Multiple Correspondence Analysis (MCA) is proposed to score the features for feature selection. Based on these scores, a ranked list of features can be generated and different selection criteria can be adopted to select a subset of features. To evaluate the proposed framework, four other wellknown feature selection methods, namely information gain, chisquare measure, correlation-based feature selection, and relief are compared with the proposed method over five popular classifiers using the benchmark data from TRECVID 2009 highlevel feature extraction task. The results show that the proposed method outperforms the other methods in terms of classification accuracy, the size of feature subspace, and the ability to capture the semantic information.

Correlation-based re-ranking for semantic concept detection

“…Multiple correspondence analysis (MCA) has been proven to perform well on many research topics, such as feature selection [10], discretization [11], video semantic concept detection [12], [13], [14], [15], and data pruning [16], which motivates us to apply it in capturing concept correlations for re-ranking process.…”

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

“…Multimedia Temporal Analysis and Ensemble Learning: MCA (Multiple Correspondence Analysis) has been successfully applied to various multimedia analysis tasks, such as feature selection [11], discretization [12], data pruning [13], classification [14] and video semantic concept detection [15]. Inspired by our HCFG algorithm and the information gain analysis method, an IF-MCA modeling approach is proposed with MapReduce implementation to deal with large-scale multimedia data.…”

“…Univariate methods, such as information gain and chi-square measure [50,54], consider the effect of each feature on a class separately without considering the inter-dependence among features. By contrast, multivariate methods, such as correlation-based feature selection [55], take features' interdependence into account.…”

“…MCA (Multiple Correspondence Analysis) has been successfully applied to various multimedia analysis tasks like feature selection [11], discretization [12], data pruning [13], classification [14,181], and video semantic concept detection and retrieval [15][8], etc., as shown in Figure 5.2. In this framework, the proposed IF generation method is integrat-ed with the MCA algorithm (i.e., IF-MCA) to perform semantic concept detection.…”

Integrating Deep Learning with Correlation-based Multimedia Semantic Concept Detection

“…Multiple Correspondence Analysis (MCA) has been applied and proven effective to the research areas ranging from feature selection [236], discretization [237], video semantic concept detection [110-115, 118, 181], to data pruning [117]. In this paper, our previous work [236] is integrated as a preprocessing step, which has been demonstrated to outperforms other existing feature selection methods, such as information gain (IG), Chi-Square measure (CHI), etc., in terms of classification accuracy.…”