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Digital Image Processing

D. Sundararajan

Chapter 12

Object Classification - all with Video Answers

Educators


Chapter Questions

03:21

Problem 1

The feature vectors of three classes are given. Using the 3-nearest neighbors classifier, classify the test vector $\{16,16\}$.
$$
\begin{array}{l|l|l}
\hline \text { Class } & \text { Feature 1 } & \text { Feature 2 } \\
\hline \text { Class1 } & 14 & 16 \\
\hline \text { Class1 } & 14 & 14 \\
\hline \text { Class1 } & 13 & 17 \\
\hline \text { Class2 } & 18 & 14 \\
\hline \text { Class2 } & 17 & 16 \\
\hline \text { Class2 } & 17 & 15 \\
\hline \text { Class3 } & 14 & 17 \\
\hline \text { Class3 } & 17 & 19 \\
\hline \text { Class3 } & 15 & 17 \\
\hline
\end{array}
$$

Runpeng Li
Runpeng Li
Numerade Educator
03:21

Problem 2

The feature vectors of three classes are given. Using the 3-nearest neighbors classifier, classify the test vector $\{16,17\}$.
$$
\begin{array}{l|l|l}
\hline \text { Class } & \text { Feature 1 } & \text { Feature 2 } \\
\hline \text { Class1 } & 15 & 16 \\
\hline \text { Class1 } & 16 & 19 \\
\hline \text { Class1 } & 15 & 17 \\
\hline \text { Class2 } & 17 & 14 \\
\hline \text { Class2 } & 18 & 15 \\
\hline \text { Class2 } & 17 & 15 \\
\hline \text { Class3 } & 14 & 17 \\
\hline \text { Class3 } & 17 & 18 \\
\hline \text { Class3 } & 14 & 16 \\
\hline
\end{array}
$$

Runpeng Li
Runpeng Li
Numerade Educator
03:21

Problem 3

The feature vectors of three classes are given. Using the 3-nearest neighbors classifier, classify the test vector $\{17,16\}$.
$$
\begin{array}{l|l|l}
\hline \text { Class } & \text { Feature 1 } & \text { Feature 2 } \\
\hline \text { Class1 } & 15 & 17 \\
\hline \text { Class1 } & 16 & 20 \\
\hline \text { Class1 } & 14 & 14 \\
\hline \text { Class2 } & 20 & 13 \\
\hline \text { Class2 } & 17 & 14 \\
\hline \text { Class2 } & 18 & 15 \\
\hline \text { Class3 } & 16 & 16 \\
\hline \text { Class3 } & 13 & 18 \\
\hline \text { Class3 } & 16 & 19
\end{array}
$$

Runpeng Li
Runpeng Li
Numerade Educator
03:49

Problem 4

Let the mean feature vectors of three classes be $\{10,10\},\{-10,-10\}$, and $\{10,-10\}$. Let the test vectors be $\{12,-10\},\{8,9\}$, and $\{-9,-11\}$. Find the three discriminant functions of the three classes. Classify the test vectors.

Anthony Ramos
Anthony Ramos
Numerade Educator
02:22

Problem 5

Let the mean feature vectors of three classes be $\{22,-20\},\{20,20\}$, and $\{-18,-19\}$. Let the test vectors be $\{23,-20\},\{21,18\}$, and $\{-18,-19\}$. Find the three discriminant functions of the three classes. Classify the test vectors.

Lisa Tarman
Lisa Tarman
Numerade Educator
03:12

Problem 6

Let the mean feature vectors of three classes be $\{15,-17\},\{17,15\}$, and $\{-16,16\}$. Let the test vectors be $\{18,-16\},\{18,14\}$, and $\{-16,16\}$. Find the three discriminant functions of the three classes. Classify the test vectors.

Rishi Kavikondala
Rishi Kavikondala
Numerade Educator

Problem 7

Draw the decision tree flowchart for classifying the five objects.
$$
\begin{array}{l|l|l|l|l|l}
\hline \text { Object } & \text { F } & \text { O } & \text { U } & \text { R } & \text { S } \\
\hline \text { Area } & 750 & 964 & 647 & 1084 & 658 \\
\hline \text { Form } & 132 & 484 & 108 & 233 & 116 \\
\hline
\end{array}
$$

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Problem 8

Draw the decision tree flowchart for classifying the five objects.
$$
\begin{array}{l|l|l|l|l|l}
\hline \text { Object } & 5 & 6 & 7 & 8 & 9 \\
\hline \text { Area } & 1199 & 1245 & 822 & 1478 & 1229 \\
\hline \text { Form } & 168 & 327 & 205 & 445 & 327 \\
\hline
\end{array}
$$

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Problem 9

Draw the decision tree flowchart for classifying the five objects.
$$
\begin{array}{l|l|l|l|l|l}
\hline \text { Object } & 0 & 1 & 2 & 3 & 4 \\
\hline \text { Area } & 1419 & 880 & 1090 & 1023 & 1118 \\
\hline \text { Form } & 576 & 273 & 183 & 176 & 402 \\
\hline
\end{array}
$$

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Problem 10

Samples of training data of two classes, $c 1(m, n)$ and $c 2(m, n)$, are
$$
c 1(m, n)=\left[\begin{array}{l}
0.7124-1.0637 \\
1.0219-1.3503 \\
1.0487-0.6465 \\
1.7837-0.5444 \\
2.4486-0.3089 \\
1.4430-0.6230 \\
0.8010-0.9970 \\
1.5931-1.0655
\end{array}\right] \quad c 2(m, n)=\left[\begin{array}{rr}
-1.2296 & 0.7281 \\
-1.2066 & 0.6124 \\
-1.7524 & 3.1638 \\
1.3789 & 0.6478 \\
-2.2385 & -0.5050 \\
-1.6842 & 3.2676 \\
-2.0069 & 2.7461 \\
-2.6606 & 0.6753
\end{array}\right]
$$
Classify the test samples $t(m, n)$ using Bayes classification.
$$
t(m, n)=\left[\begin{array}{rr}
0.4804 & -1.2375 \\
1.0979 & -0.8694 \\
-1.7471 & 0.1364 \\
-0.8781 & 0.2799 \\
-0.9416 & 0.6194 \\
-2.9676 & 1.2279
\end{array}\right]
$$
with
(i) $p\left(\omega_1\right)=0.9$ and $p\left(\omega_2\right)=0.1$.
(ii) $p\left(\omega_1\right)=0.1$ and $p\left(\omega_2\right)=0.9$.

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Problem 11

Samples of training data of two classes, $c 1(m, n)$ and $c 2(m, n)$, are
$$
c 1(m, n)=\left[\begin{array}{r}
-1.3587-1.7484 \\
1.5513-0.9966 \\
-1.0799-1.7673 \\
-1.2003-1.9427 \\
1.0758-0.6775 \\
0.1002-1.2125 \\
1.3904-0.5126 \\
1.6422-0.7607
\end{array}\right] \quad c 2(m, n)=\left[\begin{array}{rr}
-1.2082 & -0.4353 \\
0.4252 & 0.3338 \\
-4.0033 & 1.1938 \\
-1.7136 & 0.5872 \\
-2.7969 & 1.4268 \\
-1.5411 & 1.5656 \\
-0.0826 & -0.3152 \\
0.1678 & 0.7499
\end{array}\right]
$$
Classify the test samples $t(m, n)$ using Bayes classification.
$$
t(m, n)=\left[\begin{array}{rr}
-2.5374 & -2.4336 \\
1.7707 & -0.8282 \\
-1.7326 & 2.1029 \\
-3.5297 & 1.9491 \\
-1.3760 & -0.9096 \\
-2.2018 & 1.1494
\end{array}\right]
$$
with
$p\left(\omega_1\right)=0.5$ and $p\left(\omega_2\right)=0.5$.

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Problem 12

Samples of training data of two classes, $c 1(m, n)$ and $c 2(m, n)$, are
$$
\begin{aligned}
& c 1(m, n)=\left[\begin{array}{l}
2.6140-0.5545 \\
0.5164-1.0168 \\
0.9973-1.4846 \\
1.8727-0.5267 \\
1.1421-0.6542 \\
2.3328-0.3331 \\
1.9811-0.2564 \\
1.2766-0.1643
\end{array}\right] \\
& c 2(m, n)=\left[\begin{array}{rr}
0.3066 & 1.8237 \\
-0.6225 & -0.4234 \\
-0.0926 & 1.0912 \\
-0.3983 & 1.8489 \\
-2.8593 & -0.9255 \\
-1.5889 & 1.4916 \\
0.7320 & 0.7428 \\
-1.0616 & -0.3287
\end{array}\right] \\
&
\end{aligned}
$$
Classify the test samples $t(m, n)$ using Bayes classification.
$$
t(m, n)=\left[\begin{array}{rr}
1.5646 & -1.3982 \\
0.2250 & -1.5204 \\
-1.8283 & -1.3793 \\
-2.0365 & 0.0497 \\
-2.6668 & 0.3246 \\
-0.3692 & 0.8388
\end{array}\right]
$$
with
$p\left(\omega_1\right)=0.4$ and $p\left(\omega_2\right)=0.6$.

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