## 3 Proposed Work
### State Space Model
Figure 12 compares various feature identification methods, including the proposed work, with minEigen, FAST, BRICK, and SURF. [PERSON]'s accuracy is 86.5%, FAST accuracy is 89.2%, BRISK accuracy is 86.2%, SURF accuracy is 91.7%, and Mono-model accuracy is 92%. [PERSON]'s Timing is 60 ms, FAST Timing is 30 ms, BRISK Timing is 50 ms, SURF Timing is 50 ms, and Mono-model Timing is 20 ms.

Figure 11: Rate of misclassification. Figure 12: Accuracy Measure for feature identification. ### Impact of Neighborhood Selection

To investigate the influence of neighborhood selection on the classification of individual points, we evaluate 21 geometric attributes for each of the 7 alternative neighborhood definitions using 10 classifiers from various categories. Tables 6 and 7 provide a thorough investigation of the effect of neighborhood selection on classification outcomes by displaying the recall and accuracy values for the various neighborhood descriptions and classifiers. Additionally, Table 6 provides the absolute and relative processing times for the training and testing phases, enabling an assessment of the efficiency of the classifiers involved. Probabilistic learning involves inferring the most likely class label for each observed feature vector from a specified probabilistic model. One example of a probabilistic classifier is the Naive Bayes (NB) classifier, which relies on [PERSON] theorem and assumes that all features are conditionally independent ([PERSON] and [PERSON], 1995). In classification, a feature vector from the experimental set Y is given a class label that is most likely to accurately reflect its content by utilizing the class probabilities and conditional probabilities for the presence of a class provided a particular class label, both of which are determined by reference to the training set X. However, this method cannot accurately model correlated features as it assumes conditional independence. An alternative approach is to use distribution-based Bayesian Discriminant Analysis to construct a traditional maximum likelihood (ML) classifier. During the training phase of a Gaussian-based classifier, the variables of a Gaussian distribution are assessed for each class using either multivariate Gaussian distribution or parameter fitting. The Linear Discriminant Analysis (LDA) classifier assumes that each class has the same covariance matrix, with only the means varying. On the other hand, the \"Quadratic Discriminant Analysis (QDA) classifier\" allows the covariance matrix for each individual class to differ. The likelihood of a feature vector belonging to each class is calculated, and the class with the greatest likelihood of accuracy is selected when classifying a new feature vector. Figure 13 compares various classifiers and their accuracy levels for different neighborhood definitions. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Oakland** & **NN** [48] & **DT** [48] & **NB** [48] & **LDA** [48] & **QDA** [48] & **SVM** [48] & **MLP** [48] & **SOM** \\ \hline N 10 & 73.86 & 65.64 & 78.88 & 87.38 & 78.93 & 85.69 & 80.54 & 82.54 \\ N 25 & 86.25 & 69.3 & 83.64 & 90.08 & 83.62 & 88.88 & 78.59 & 85.62 \\ N 50 & 88.89 & 75.47 & 85.03 & 92.83 & 84.95 & 92 & 85.68 & 89.03 \\ N 75 & 89.97 & 76.87 & 85 & 93.05 & 84.99 & 91.99 & 87.07 & 91.87 \\ N 100 & 89.9 & 84.45 & 84.33 & 92.6 & 84.43 & 91.76 & 84.39 & 88.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Overall accuracy (in %) for different neighborhood definitions and different classifiers. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Finding Models** & **Accuracy (\%)** & **Timing (ms)** \\ \hline MinEigan & 86.5 & 60 \\ FAST & 89.2 & 30 \\ BRISK & 86.2 & 50 \\ SURF & 91.7 & 50 \\ Mono-model & 92 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Feature Identification Schemes. Precision is the level of a classifier's conformance and accuracy when measured against a real or absolute value.