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\item $h_i^{bg} = \sum_{j \notin c_i} bow(f_j)$ - a frequency of quantized local features $f_j$ extracted outside the region $c_i$.  \end{itemize}  \subsubsection{Performance Evaluation}  \subsubsection{UCF-Sport Dataset}  \subsubsection{$mAP$ performance with different types of kernel functions vs background histogram weight values, $\alpha_{bg}$ }  \begin{tabular}{| c | c | c | c |}  \textbf{Kernel Type} & $\alpha_{bg} = 0$ & $\alpha_{bg} = 0.5$ & $\alpha_{bg} = 0.75$ \\   $Linear$ & 31.91 \% & 56.69 \% & 65.29 \% \\   $Intersection$ & 35.65 \% & 58.98 \% & 60.89 \% \\   $Chi-Square$ & 39.03 \% & 63.08 \% & 65.96 \% \\   $Jenson-Shannon$ & 39.50 \% & 63.53 \% & \\   \end{tabular}  \subsubsection{The runtime evaluation (in mins) with different types of kernel functions vs background histogram weight values, $\alpha_{bg}$ }  \begin{tabular}{| c | c | c | c |}  \textbf{Kernel Type} & $\alpha_{bg} = 0$ & $\alpha_{bg} = 0.5$ & $\alpha_{bg} = 0.75$ \\   $Linear$ & 2.5 & 22.3 & 15.1 \\   $Intersection$ & 3.3 & 20.3 & 16.8 \\   $Chi-Square$ & 2.4 & 14.8 & 11.2 \\   $Jenson-Shannon$ & 3.0 & 78.7 & \\   \end{tabular}