1. Jaruloj Chongstitvatana2301474 Advanced Data Structures 1 Index Structures for Multimedia Data Feature-based Approach

2. 2301474 Advanced Data Structures2 Jaruloj Chongstitvatana Multimedia Data Feature-based approach Image/Voice data Sequence data Geometric data Text descriptor Examples Movies, music Gene sequence Shape (CAD) Documents

3. 2301474 Advanced Data Structures3 Jaruloj Chongstitvatana Queries for Multimedia Data Point queries  Given a data, find the exact match Range queries  Given a data, find similar data within a range Nearest-neighbor queries  Given a data, find the most similar data

4. 2301474 Advanced Data Structures4 Jaruloj Chongstitvatana Feature Transformation Mapping from an object to a d-dimensional vector, called a feature vector. What is this mapping function?  For image data: color histogram, etc.  For sequence data: number of each element  For geometric data: slope of segments of perimeter  For text descriptor: number of each keyword

5. 2301474 Advanced Data Structures5 Jaruloj Chongstitvatana Similarity Measure: distance function Given 2 data objects x and y. Let  (x,y) be the distance function.   (x,y) indicates the similarity between data x and y. Usually  (x,y) is based on a distance between the feature vectors of x and y.

6. 2301474 Advanced Data Structures6 Jaruloj Chongstitvatana Similarity Queries Point queries  Given an object x, find any object y such that  (x,y)=0. Range queries  Given an object x and a threshold , find any object y such that  (x,y) < . Nearest-neighbor queries  Given an object x, find an object y such that  (x,y) ≤  (x,z) for any object z in the database.

7. 2301474 Advanced Data Structures7 Jaruloj Chongstitvatana Distance Measure Euclidean distance Manhattan distance Maximum distance Weighted Euclidean distance Ellipsoid distance  (x,y) = (  i=1,…,d (x i -y i ) 2 ) 1/2  (x,y) =  i=1,…,d |x i -y i |  (x,y) = max i=1,…,d |x i -y i |  (x,y) = (  i=1,…,d w i (x i -y i ) 2 ) 1/2  (x,y) = (x-y) T W (x-y)

8. 2301474 Advanced Data Structures8 Jaruloj Chongstitvatana Other Similarity Queries k-Nearest-neighbor queries  Given an object x and an integer k, find k objects y 1, y 2,…, y k, such that, for i=1, 2, …, k,  (x,y i ) ≤  (x,z) for any other object z in the database. Approximate nearest-neighbor queries Approximate k-nearest-neighbor queries

9. 2301474 Advanced Data Structures9 Jaruloj Chongstitvatana Range Queries On k-d-B trees Grid files Quad trees R-trees Already discussed.

10. 2301474 Advanced Data Structures10 Jaruloj Chongstitvatana Nearest-neighbor Queries On k-d-B trees Grid files Quad trees R-trees Let’s discuss.