Abstract
The main objective of image browsers is to empower users to find a desired image with ease, speed and accuracy from a large database. In this paper we present a novel approach at creating an image browsing environment based on human perception with the aim of providing intuitive image navigation. In our approach, similarity judgments form the basic structural organization for the images in our browser. To enrich this we have developed a scalable crowd sourced method of augmenting a database with a large number of additional samples by capturing human judgments from members of a crowd. Experiments were conducted involving two databases that demonstrate the effectiveness of our method as an intuitive, fast browsing environment for large image databases.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 169-176 |
| Number of pages | 8 |
| Volume | 8048 LNCS |
| Edition | PART 2 |
| DOIs | |
| Publication status | Published - 26 Sep 2013 |
| Event | 15th International Conference on Computer Analysis of Images and Patterns - York, United Kingdom Duration: 27 Aug 2013 → 29 Aug 2013 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART 2 |
| Volume | 8048 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Conference
| Conference | 15th International Conference on Computer Analysis of Images and Patterns |
|---|---|
| Abbreviated title | CAIP 2013 |
| Country | United Kingdom |
| City | York |
| Period | 27/08/13 → 29/08/13 |
Fingerprint
Keywords
- Abstracts
- Browsers
- Clustering
- Crowd Sourcing
- Databases
- Images
- Indexing
- Navigation
- Perception
- Retrieval
- Similarity
- Textures
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
}
Intuitive large image database browsing using perceptual similarity enriched by crowds. / Padilla, Stefano; Halley, Fraser; Robb, David A.; Chantler, Mike J.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8048 LNCS PART 2. ed. 2013. p. 169-176 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8048 LNCS, No. PART 2).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Intuitive large image database browsing using perceptual similarity enriched by crowds
AU - Padilla, Stefano
AU - Halley, Fraser
AU - Robb, David A.
AU - Chantler, Mike J.
PY - 2013/9/26
Y1 - 2013/9/26
N2 - The main objective of image browsers is to empower users to find a desired image with ease, speed and accuracy from a large database. In this paper we present a novel approach at creating an image browsing environment based on human perception with the aim of providing intuitive image navigation. In our approach, similarity judgments form the basic structural organization for the images in our browser. To enrich this we have developed a scalable crowd sourced method of augmenting a database with a large number of additional samples by capturing human judgments from members of a crowd. Experiments were conducted involving two databases that demonstrate the effectiveness of our method as an intuitive, fast browsing environment for large image databases.
AB - The main objective of image browsers is to empower users to find a desired image with ease, speed and accuracy from a large database. In this paper we present a novel approach at creating an image browsing environment based on human perception with the aim of providing intuitive image navigation. In our approach, similarity judgments form the basic structural organization for the images in our browser. To enrich this we have developed a scalable crowd sourced method of augmenting a database with a large number of additional samples by capturing human judgments from members of a crowd. Experiments were conducted involving two databases that demonstrate the effectiveness of our method as an intuitive, fast browsing environment for large image databases.
KW - Abstracts
KW - Browsers
KW - Clustering
KW - Crowd Sourcing
KW - Databases
KW - Images
KW - Indexing
KW - Navigation
KW - Perception
KW - Retrieval
KW - Similarity
KW - Textures
U2 - 10.1007/978-3-642-40246-3_21
DO - 10.1007/978-3-642-40246-3_21
M3 - Conference contribution
SN - 9783642402456
VL - 8048 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 169
EP - 176
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -