Title: Image Database Design Based on 9DSPA Representations for Spatial Relations
1Image Database Design Based on 9D-SPA
Representations for Spatial Relations
Paper review
Po-Whei Huang, Chu-Hui Lee. IEEE Transactions on
Knowledge and Data Engineering, vol. 16, no. 12,
pp. 1486-1496, December, 2004.
- James M. Kang
- Liang Guo
- 8715 Spatial Databases
- http//www-users.cs.umn.edu/jkang/8715/
2Outline
- Motivation Problem Statement
- Contributions
- Key Concepts
- Validation Methodologies
- Assumptions
- Rewrite today
3 Motivation
- Encoding the spatial relations in an image to
facilitate content based image retrieval in a
pictorial database. - Pictorial database Spatial relations
- Content based image retrieval
- Finding all the pictures containing a house to
the west of a car
4 Motivation
- Application domain
- GIS
- CAD
- Office Automation
- Medical image archiving
- Trademark picture registration
- Applied to any image database domain
- Incl. Photographs
5 Problem Statement
- Given
- A set of pictures P, each p containing n objects
(O1, O2, .., On) where each o corresponds to an
icon i - Find
- A set of similar images based on a query image
- Objective
- Minimize computational time to query an image
index - Constraints
- Objects in an image are pre-determined
- Objects are assigned to a pre-determined Icon
- The number of Icons in the database are fixed
- Each image must contain at least 2 objects
6Outline
- Motivation Problem Statement
- Contributions
- Key Concepts
- Validation Methodologies
- Assumptions
- Rewrite today
7 Contributions
- Proposed a novel algorithm
- 9-Directional Spanning Area (9D-SPA)
- supports visualization, browsing, spatial
reasoning, icon indexing and similarity retrieval - Features of 9D-SPA
- Spatial reasoning and interpretation
- Visualization and database browsing
- A set of coarse-to-fine similarity for iconic
indexing
8Outline
- Motivation Problem Statement
- Contributions
- Key Concepts
- Validation Methodologies
- Assumptions
- Rewrite today
9 Key Concepts
- Indexing
- A method to efficiently and effectively store
data to easily retrieve the information - Spatial Operators
- Several relationships between two objects
10 Key Concepts
- Text-based vs. Content-based Image Search
- Text-based image annotations
- Example
- Google image search superman or super man
- Content-based image and the information within
images - Search image looks like above image in a database
- finger print matching
- suspect facial image matching,
11 Key ConceptsQuestion
- Which of the following queries are best suited
for a text- or a content-based engine? - Finger Print Matching?
- Thanksgiving related images?
- Suspect Facial Image Matching?
12 Key Concepts Related work
- 2D string (1987)Project an object along its x
and y directions to form two strings representing
the relative positions of objects in the x and
y-axis. - 9DLT 5 Topological relations (1997)
- 9 direction lower-triangular (9DLT) Disjoint,
meet, partly overlap, contain and inside - MOBR v.s. Centroid
- too sensitive to directional relations
-
13 Key ConceptsRelated work
- Applications of Content based image search system
- QBIC (Query By Image Content / IBM)
- Virage
- RetrievalWare
- VisualSEEK
- WaveGuide
- PhotoBook
14 Key Concepts Related work
- QBIChttp//www.hermitagemuseum.org/fcgi-bin/db2ww
w/qbicSearch.mac/qbic?selLangEnglish
15 Key Concepts Related work
- QuerySEEKuser forms the queries by diagramming
spatial arrangements of color regions.
http//www.ctr.columbia.edu/jrsmith/html/pubs/acm
mm96/acm.html
16 Key ConceptsContributions
- 9D-SPA
- 9 directional relations
17 Key Concepts Contributions
- 9D-SPA
- 5 topological relations
- 0 Disjoint
- 1 Meet
- 2 Partly-overlap
- 3 Cover
- 4 Contain or inside
18 Key Concepts Contributions
19 Key Concepts Contributions
- Example
- Image visualization
- Similarity measure
- Directional similarity p2 to p1 is 0.67
- Topological similarity p2 to p1 is 1
20Outline
- Motivation Problem Statement
- Contributions
- Key Concepts
- Validation Methodologies
- Assumptions
- Rewrite today
21 Validation Methodologies
- Experiments were performed on simulated synthetic
data - Advantages
- Able to measure the efficiency and effectiveness
accurately of their algorithms - Disadvantages
- The practicality of the system is unclear since
no real data sets were used
22Outline
- Motivation Problem Statement
- Contributions
- Key Concepts
- Validation Methodologies
- Assumptions
- Rewrite today
23 Assumptions
- Objects can be found beforehand through some form
of image segmentation or object recognition - Object recognition is a VERY hard problem
- Thus, those techniques should be clearly defined
within the paper - An exact definition of an object is not defined
- There may be a number of objects within an image
- A clear distinction between fore- and back-ground
objects are not clear.
24Outline
- Motivation Problem Statement
- Contributions
- Key Concepts
- Validation Methodologies
- Assumptions
- Rewrite today
25 Rewrite todayTechnical
- 5 Topological spatial relationships do not seem
sufficient to image searches - Since images use a 3D environment where
topological images use a 2D environment - Possible extensions of new operators in a 3D
environment can consist of in front of or
behind - No comparisons between existing algorithms
- Apply to real world scenarios when a True Object
Detection system exists
26 Rewrite todayStructural
- Claims need to be justified
- The most important feature of a symbolic picture
is probably the binary spatial relationships
between objects - Explain image objects and icons and give general
examples before start using them
27 Rewrite todayDiscussion
- Why not Z-curve or Herbert curve in 2D string?
- Why not combine MOBR and Centroid together in
9DLT?
28 Questions?