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Hello, could you please show working out how to tackle this question. It is a question to answer using paper method. My end of year exam for Image Analysis is nearing and I have been informed questions like these will pop up.
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![The 'Skin Segmentation Dataset' contains samples of skin and non-skin pixel colours for
automated face detection in colour images. Table 1 shows only a small subset of these
samples.
(a)
(b)
Using the k-NN classification approach (for k=3, and k =7) determine whether a
pixel with colour values R=145, G=140, and B=122 should be categorised as a skin
pixel or not. Use the 'city-block distance' (aka 'Manhattan distance' or 'taxicab
geometry') metric for your calculation.
What would be the classification if the Nearest Mean Classifier (NMC) were used?
[Use the 'city-block distance' metric.]
(c) Cosine-similarity measure between two vectors A and B is defined by](https://content.bartleby.com/qna-images/question/9ecfbd04-9453-4891-92a0-8c48cc184af7/fc4f55cf-2c19-4667-bd9b-2dc23b389a6f/g0j4vu_processed.png)
Transcribed Image Text:The 'Skin Segmentation Dataset' contains samples of skin and non-skin pixel colours for
automated face detection in colour images. Table 1 shows only a small subset of these
samples.
(a)
(b)
Using the k-NN classification approach (for k=3, and k =7) determine whether a
pixel with colour values R=145, G=140, and B=122 should be categorised as a skin
pixel or not. Use the 'city-block distance' (aka 'Manhattan distance' or 'taxicab
geometry') metric for your calculation.
What would be the classification if the Nearest Mean Classifier (NMC) were used?
[Use the 'city-block distance' metric.]
(c) Cosine-similarity measure between two vectors A and B is defined by
![similarity = cos(0) =
139
220
159
223
253
228
232
209
148
223
234
228
251
140
223
225
217
215
149
157
210
198
254
234
240
Skin samples (C1)
Red (R) Green (G) Blue (B)
where A, and B; are components of vector A and B respectively.
[Note: distance = (1 - similarity).]
Determine the classification result if the cosine-similarity metric is used with the
NMC scheme for the skin-colour data.
76
142
98
153
195
167
186
146
97
149
187
172
211
93
158
154
158
139
103
105
153
124
190
190
187
A.B
||A||||B||
59
104
51
104
158
120
171
113
54
100
171
123
201
51
126
102
124
87
51
48
123
61
152
155
171
Table 1
71
ΣΑ, Β,
i=1
ΣΑΣ Β
i=1
3
84
121
152
112
93
144
88
93
250
19
129
102
201
32
61
88
11
145
104
254
135
100
Non-Skin samples (C2)
Red (R) Green (G) Blue (B)
14
68
172
193
158
147
163
127
185
136
139
247
29
174
132
54
82
107
156
31
186
124
193
180
127
198
175
16
115
173
195
156
149
187
150
137
242
98
177
0
60
81
105
227
32
188
249
14
185
32
200
177](https://content.bartleby.com/qna-images/question/9ecfbd04-9453-4891-92a0-8c48cc184af7/fc4f55cf-2c19-4667-bd9b-2dc23b389a6f/293doy_processed.png)
Transcribed Image Text:similarity = cos(0) =
139
220
159
223
253
228
232
209
148
223
234
228
251
140
223
225
217
215
149
157
210
198
254
234
240
Skin samples (C1)
Red (R) Green (G) Blue (B)
where A, and B; are components of vector A and B respectively.
[Note: distance = (1 - similarity).]
Determine the classification result if the cosine-similarity metric is used with the
NMC scheme for the skin-colour data.
76
142
98
153
195
167
186
146
97
149
187
172
211
93
158
154
158
139
103
105
153
124
190
190
187
A.B
||A||||B||
59
104
51
104
158
120
171
113
54
100
171
123
201
51
126
102
124
87
51
48
123
61
152
155
171
Table 1
71
ΣΑ, Β,
i=1
ΣΑΣ Β
i=1
3
84
121
152
112
93
144
88
93
250
19
129
102
201
32
61
88
11
145
104
254
135
100
Non-Skin samples (C2)
Red (R) Green (G) Blue (B)
14
68
172
193
158
147
163
127
185
136
139
247
29
174
132
54
82
107
156
31
186
124
193
180
127
198
175
16
115
173
195
156
149
187
150
137
242
98
177
0
60
81
105
227
32
188
249
14
185
32
200
177
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