## 3 Experiments
### Human Actions Categorization  
Recently, multimedia categorization and recognition are becoming two challenging research problems that have attracted a lot of attention for several applications [49; 50; 51; 52]. Object categorization refers to the task of labelling objects into one of the predefined and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **KTH-TIPS** & **UIUCTex** & **UMD** \\ \hline GMM & 80.22 & 84.64 & 83.70 \\ inGMM & 80.94 & 85.33 & 84.14 \\ GGMM & 82.11 & 86.51 & 86.42 \\ inGGMM & 83,30 & 87.21 & 87.17 \\ MGGMM [10] & 84.10 & 88.09 & 88.15 \\ inMGNMM (our method) & 85.91 & 89.03 & 89.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall classification accuracy rate (%) of different approaches for the three texture datasets using SIFT features. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **KTH-TIPS** & **UIUCTex** & **UMD** \\ \hline GMM & 80.88 & 84.98 & 83.99 \\ inGMM & 81.24 & 86.03 & 84.84 \\ GGMM & 82.90 & 87.05 & 86.96 \\ inGGMM & 83.94 & 87.84 & 87.86 \\ MGGMM [10] & 84.81 & 88.78 & 88.87 \\ inMGNMM (our method) & 86.29 & 89.81 & 89.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall classification accuracy rate (%) of different approaches for the three texture datasets using LBP features. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **KTH-TIPS** & **UIUCTex** & **UMD** \\ \hline GMM & 83.08 & 87.77 & 86.12 \\ inGMM & 84.13 & 89.77 & 87.12 \\ GGMM & 85.76 & 90.02 & 89.75 \\ inGGMM & 86.88 & 90.67 & 90.20 \\ MGGMM [10] & 87.67 & 91.07 & 91.23 \\ inMGNMM (our method) & 88.91 & 92.11 & 92.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overall classification accuracy rate (%) of different approaches for the three texture datasets using DMD features. meaningful categories and this step is mainly based on extracting effective visual features. Image categorization plays an essential role in many applications such as automatic organization, activity recognition, object retrieval and tracking. In particular, recognizing human activities (HAR) is extremely recommended for security purposes (video surveillance), health care, robotics, and so forth. The objective is to identify the actions of one or more persons and then to analyze them [53]. It is worth noting that manually categorizing a big dataset is impractical and time consuming. Recently, various automatic and semi-automatic methods have been developed to address this problem [10; 12; 54; 55; 56]. For instance, a finite generalized Gaussian mixture model is investigated to deal with HAR through a complex real dataset [10; 12; 52]. In [57], the authors developed an algorithm to identify human gestures using features derived from key poses. A Hidden Markov Model is also presented for HAR in [58]. It is noted that, for a human, a simple glance is enough to classify or categorize any action. Nevertheless, exact and accurate categorization with a computer (or algorithm) is a very difficult task and it will be more complex in the presence of illumination, occlusions, lighting, noise, the and absence of a large amount of labelled data. Thus, increasing attention has been paid to unsupervised machine learning methods, which are able to learn objects from unlabelled data. Furthermore, implementing more advanced computational intelligence techniques is required to solve the above issues and to achieve higher recognition rates. In this work, our contribution consists of exploiting our approach for human activity categorization and recognition. For this goal, the KTH human action dataset [59] was used to evaluate its performance. Some samples of representative actions with four scenarios are presented in Figure 4. These scenarios are outdoors (s1), outdoors with scale variation (s2), outdoors with different clothes (s3) and indoors (s4). The dataset basically contains 2391 sequences with different actions that are grouped into six categories--running, boxing, walking, jogging, hand waving and hand clapping.