1. 多媒體資料庫

13. 範例

29. 插入 p 14 R1 R2 R3 R4 R5 p6 p7 p5 p1 p2 Pointers to data tuples p8 p3 p4 p9 p10 p11 p12 p13 R6 R7 R3 R4 R5 R6 R7 p1 p7 p6 p8 p2 p3 p4 p5 p9 p10 p11 p12 p13 R1 R2 p14

30. inserting p14 R1 R2 R3 R4 R5 p6 p7 p5 p1 p2 Pointers to data tuples p8 p3 p4 p9 p10 p11 p12 p13 R6 R7 R3 R4 R5 R6 R7 p1 p7 p6 p8 p2 p3 p4 p5 p9 p10 p11 p12 p13 p14 p14 R1 R2

38. P 6 P 10 P 9 P 8 P 7

46. 演算法

71. Color model RGB color space v.s. HSV color space

77. 範例 SIZE = 13 + 10 + 5 = 28 Query image A 13 B 10 C 5

87. The Correlative-Matrix(2) Construction of correlative matrix T 12,12 -- C6 -- Bb5 1 -- C6 -- Db5 1 1 -- C6 -- Ab5 1 -- Ab5 1 1 1 -- C6 1 1 4 1 -- C6 3 1 -- Ab5 1 2 1 -- Ab5 1 1 1 1 1 -- C6 C6 Bb5 C6 Db5 C6 Ab5 Ab5 C6 C6 Ab5 Ab5 C6

107. Training Data Classification Algorithms IF rank = ‘professor’ OR years > 6 THEN tenured = ‘yes’ Classifier (Model)

108. Classifier Testing Data Unseen Data (Jeff, Professor, 4) Tenured?

110. 訓練資料集 (training data set)

111. 決策樹 age? overcast student? credit rating? no yes fair excellent <=30 >40 no no yes yes yes 30..40

112. Naïve bayesian Network :example P(n) = 5/14 P(p) = 9/14

113. P(true|n) = 3/5 P(true|p) = 3/9 P(false|n) = 2/5 P(false|p) = 6/9 P(high|n) = 4/5 P(high|p) = 3/9 P(normal|n) = 2/5 P(normal|p) = 6/9 P(hot|n) = 2/5 P(hot|p) = 2/9 P(mild|n) = 2/5 P(mild|p) = 4/9 P(cool|n) = 1/5 P(cool|p) = 3/9 P(rain|n) = 2/5 P(rain|p) = 3/9 P(overcast|n) = 0 P(overcast|p) = 4/9 P(sunny|n) = 3/5 P(sunny|p) = 2/9 windy humidity temperature outlook

115. Bayesian Belief Networks Family History LungCancer PositiveXRay Smoker Emphysema Dyspnea LC ~LC (FH, S) (FH, ~S) (~FH, S) (~FH, ~S) 0.8 0.2 0.5 0.5 0.7 0.3 0.1 0.9 Bayesian Belief Networks The conditional probability table for the variable LungCancer

116. Bayesian Belief Networks

117. The k -Nearest Neighbor Algorithm . _ + _ x q + _ _ + _ _ + . . . . .

119. Fuzzy set approach

123. 範例 Database D Scan D C 1 L 1 L 2 C 2 C 2 Scan D C 3 L 3 Scan D

126. {} f:4 c:1 b:1 p:1 b:1 c:3 a:3 b:1 m:2 p:2 m:1 Header Table Item frequency head f 4 c 4 a 3 b 3 m 3 p 3

128. Step 1: 對 FP-tree 內的每個 node, 建置 conditional pattern base Conditional pattern bases item cond. pattern base c f:3 a fc:3 b fca:1, f:1, c:1 m fca:2, fcab:1 p fcam:2, cb:1 {} f:4 c:1 b:1 p:1 b:1 c:3 a:3 b:1 m:2 p:2 m:1 Header Table Item frequency head f 4 c 4 a 3 b 3 m 3 p 3

130. Mining Frequent Patterns by Creating Conditional Pattern-Bases Empty Empty f {(f:3)}|c {(f:3)} c {(f:3, c:3)}|a {(fc:3)} a Empty {(fca:1), (f:1), (c:1)} b {(f:3, c:3, a:3)}|m {(fca:2), (fcab:1)} m {(c:3)}|p {(fcam:2), (cb:1)} p Conditional FP-tree Conditional pattern-base Item

131. Step 3: Recursively mine the conditional FP-tree Cond. pattern base of “am”: (fc:3) Cond. pattern base of “cm”: (f:3) {} f:3 cm-conditional FP-tree Cond. pattern base of “cam”: (f:3) {} f:3 cam-conditional FP-tree {} f:3 c:3 a:3 m-conditional FP-tree {} f:3 c:3 am-conditional FP-tree

132. 效能分析 Data set T25I20D10K

139. Content-Based Interactivity

141. Paper study : topic 1 A Semantic Modeling Approach for Video Retrieval by Content Edoardo Ardizzone Mohand-Said Hacid ICMCS 1999 July

145. Schema Language—example1

151. Introduction --Typical sequence of shots in sports video

153. Classifying visual shot features

154. Implementation --Classifying visual shot features (cont.)

157. Paper study: topic 2 Indexing methods for approximate string matching IEEE data engineering bulletin,2000 Gonzalo Navarro, Ricardo Baeza-Yates, Erkki Sutinen, Jorma Tarhio

161. Suffix trees 1 g a a c c g a c c t 2 a a c c g a c c t 3 a c c g a c c t 4 c c g a c c t 5 c g a c c t 6 g a c c t 7 a c c t 8 c c t 9 c t 10 t Weak point:large space requirement,about 9 times of text size.

162. Suffix array Require less space,about 4 times of text size a $ a a a a b b c d r a b b c d r r a a r r a a $ c a a c $ $ c

163. Q-grams,Q-samples TEXT 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 INDEX a b r a b r a c r a c a a c a d c a d a 1 8 2 3 4 5 Q-samples,unlike q-grams, do not overlap , and may even be some space between each pair of samples. a b r a c a d a b r a

164. Edit distance ed(“SURVEY”,”SURGERY”) Final result 2 2 2 3 3 4 5 6 Y 3 2 1 2 2 3 4 5 E 4 3 2 1 1 2 3 4 V 4 3 2 1 0 1 2 3 R 5 4 3 2 1 0 1 2 U 6 5 4 3 2 1 0 1 S 7 6 5 4 3 2 1 0 Y R E G R U S

166. Neighborhood generation Pattern :abc with 1 error { ＊ bc, a ＊ c,ab ＊ } U {ab,ac,bc} U{ ＊ abc,a ＊ bc,abc ＊ } Text a b r a c a d a b r a {abr},{ac},{abr},.. results K-Neignborhood K-neighborhood(candidate) could be quite large, So,this approach works well for small m and k. searching

168. Partitioning into exact search Pattern :abr with 1 error {a},{br} Text a b r a c a d a b r a {abra},{abra}.. results Partition pattern 1.For large error level the text areas to verify cover almost almost all the text. 2.If s grow,pieces get shorter, more match to check,but make the filter stricter. Exact search verification Text a b r a c a d a b r a into (K+s) pieces filtration

170. Intermediate Partitioning Pattern :abr with 1 error {a},{br} Text a b r a c a d a b r a {abra},{abra}.. results Partition pattern Neighborhood generation allow floor of k/j verification Text a b r a c a d a b r a into j (j=2)pieces J=2 (j=K+1;partitioning into exact search) searching

171. Intermediate Partitioning Pattern :abr with 1 error {abr} Text a b r a c a d a b r a {abra},{abra}.. results Partition pattern 1.Which j value to use? the search time decreases when j move from 1 to k+1. but the verification cost grows, oppositiely. Neighborhood generation allow floor of k/j into j (j=1)pieces J=1 (neighborhood generation) searching {*abr,a*br,ab*r,abr*}U {ab,br,ar}U{ab*,*br,a*r}

173. summarization

174. Paper study: topic 3 Lazy Users and automatic Video Retrieval Tools in (the) Lowlands The Lowlands Team CWI 1 , TNO 2 , University of Amsterdam 3 , University of Twente 4 The Netherlands Jan Baan 2 , Alex van Ballegooij 1 , Jan Mark Geusenbroek 3 , Jurgen den Hartog 2 , Djoerd Hiemstra 4 , Johan List 1 , Thijs Westerveld 4 , Ioannis Patras 3 , Stephan Raaijmakers 2 , Cees Snoek 3 , Leon Todoran 3 , Jeroen Vendrig 3 , Arjen P. de Vries 1 and Marcel Worring 3 . Proceeding of the 10 th Text Retrieval Conference(TREC), 2001

179. Introduction Combined 1-4, interactive, by a lazy user 5 Query articulation, interactive 4 Transcript-base, automatic 3 Combined 1-3, automatic 2 Detector-base, automatic 1 Description Run

182. Detector-base processing (cont) Selected detector Analysis of the topic description Query by example Filter-out irrelevant material Final ranked results

190. Topic 33: White fort Using Run 1: Any color-based technique worked out well for this query Example known-item keyframe

197. Paper study : topic 4 VIDEO INDEXING BY MOTION ACTIVITY MAPS Wei Zeng; Wen Gao; Debin Zhao; Image Processing. 2002. Proceedings. 2002 International Conference on , Volume: 1 , 2002 Page(s): 912 -915

212. Generation of MAM Demo video segmentation Hall shall Motion vector field Video Video Video Temporal video Segmentation MAM Computing MAM Quantization MAM spatial Segmentation MAM Region- Based MAMs

214. Organization of MAMs Interactive Video Retrieval Video Video Video Temporal Segmentation MAM Computing Layered spatial segmentation MAM display MAM Database

215. Expermental results (a)Key frame based MAM (b)MAM (c-f)Region-representation of MAM

217. Paper study : topic 5 SOM-Base R*-Tree for Similarity Retrieval Database Systems for Advanced Applications, 2001. Proceedings. Seventh International Conference on , 2001 Kun-seok. Oh, Yaokai Feng, Kunihiko Kaneko, Akifumi Makinouchi, Sang-hyun Bae

219. Self-Organizing Maps (SOM) What is SOM 1.SOM provide mapping from high-demensional feature vectors onto a two-dismensional space 2.The mapping preserves the topology of the feature vector. 3.The map is called to topological feature map , and preserves the mutual relationships(similarity) in feature space of input data. 4.The vectors contained in each node of the topological feature map are usually called codebook vectors .

221. 圖：均勻分佈之資料的自我組織特徵映射圖： (a) 隨機設定之初始鍵結值向量 ; (b) 經過 50 次疊代後之鍵結值向量 ;(c) 經過 1,000 次疊代 後之鍵結值向量 ;(d) 經過 10,000 次疊代後之鍵結值向量 ; Self-Organizing Maps (SOM)

223. Self-Organizing Maps (SOM) SOM Algorithm 1.Init Map neuron. 2.input feature vector x. 3.find winner neuron (BMN:Beat-Match Node) 4.adjusting all neuron’s weight 5.continus step 2, until no adjusting.

226. R*-Tree (cont.) Space of point data

227. R*-Tree (cont.) Tree access structure

229. SOM-Based R*-Tree (cont.)

234. Experiments (cont.)

237. Experiments (cont.)