Similarity retrieval techniques

Lee, Edward T.

doi:10.1007/3-540-09757-0_6

Cited by 17 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studying the similarity of various similarity measures. There are many ways to define a similarity measure (Changt, 1976; Lee, 1980a, b; Ward and Chien, 1979; Zadeh, 1971b). Although they are different, however, they are similar.…”

Section: Proposed Research Topicsmentioning

confidence: 99%

“…In this approach, color clusters, color codebooks, an unsupervised learning algorithm, and a nearest‐neighbor classifier are thoroughly studied (Wu, 1986). Furthermore, the relationships among pictorial data compression techniques, pictorial databases (Lee, 1980b, 1984), pictorial knowledge (Lee, 1987, 1988a, b; Lee et al , 1987a, 1989b) and pictorial information systems (Chang, 1989; Chang and Fu, 1980) are also carefully investigated.…”

Section: Proposed Research Topicsmentioning

confidence: 99%

See 1 more Smart Citation

Research topics in pictorial databases

Lee¹

2004

Kybernetes

Self Cite

View full text Add to dashboard Cite

Pictures are natural and effective means of communication among people, computers, and robotics. A pictorial database is a collection of sharable pictorial data encoded in various formats. During the past several years, pictorial databases have attracted growing attention as an important component in building pictorial information systems as well as intelligent information systems. Eight research tasks are presented. They are comparing the numerical and the linguistic variable approaches, examining new linguistic hedges, studying the similarity of various similarity measures, investigating pictorial data compression techniques, performing pictorial data compression using array grammars, applying the entity‐relationship (ER) model to picture description; further investigating the relationship hierarchy for picture representation using ER diagrams, and extracting pictorial knowledge from pictorial databases. The research results may have a major impact on the development of object‐oriented pictorial databases.

show abstract

Section: Proposed Research Topicsmentioning

confidence: 99%

Section: Proposed Research Topicsmentioning

confidence: 99%

Research topics in pictorial databases

Lee¹

2004

Kybernetes

Self Cite

View full text Add to dashboard Cite

show abstract

“…This topic offers what appears to be a fertile field for further study. The results may have useful applications in robotics, picture processing, pattern recognition, artificial intelligence, knowledge engineering, pictorial database design?- 12 and expert systems.…”

Section: Vni Conclusionmentioning

confidence: 99%

Applications of entity-relationship model to picture description

Lee

Chu

1987

Robotica

Self Cite

View full text Add to dashboard Cite

SUMMARYThe concepts of entity-relationship diagram have been applied to picture description. Primitive picture entities, picture relationships, and picture grammars are presented with illustrative examples. A high-level description of a two-level picture generation system is proposed using either string description or ER diagram description. Illustrative examples are also given. The advantages of ER diagram description together with its comparison to string description are also presented. The results may have useful applications in robotics, artificial intelligence, expert systems, picture processing, pattern recognition, knowledge engineering and pictorial database design.

show abstract

“…(1) A useful tool for database as well as similarity-driven pictorial database (Lee, 1980(Lee, , 1984 design.…”

Section: Introductionmentioning

confidence: 99%

Similarity‐driven pictorial database design using entity‐relationship approach

Lee¹,

Lee²

1999

Kybernetes

Self Cite

View full text Add to dashboard Cite

The entity‐relationship approach is applied to similarity‐driven pictorial database design. The capabilities and the limitations of computers are presented. Advantages of the entity‐relationship approach, and similarity‐driven pictorial database design are also presented. Classification of pictures, similarity‐driven face databases, chromosome databases, leukocyte databases are investigated. Quantitative measure of equal‐perimeter circular shape, equal‐area circular shape, elongated, spiculed, indented, slightly indented, and deeply indented leukocytes are also investigated. The entity‐relationship diagrams of a house, a house with high roof, and a church are shown as illustrative examples of this approach. The class of elongated isosceles triangles is viewed as a “fuzzy entity”. Topics for future research are also stated. The results have useful applications in data engineering, knowledge engineering, pattern recognition, artificial intelligence, fuzzy language theory, computer graphics, expert systems and pictorial database design.

show abstract

Similarity retrieval techniques

Cited by 17 publications

References 30 publications

Research topics in pictorial databases

Research topics in pictorial databases

Applications of entity-relationship model to picture description

Similarity‐driven pictorial database design using entity‐relationship approach

Contact Info

Product

Resources

About