### Abstract

Many data partitioning index methods perform poorly in high dimensional space and do not support relevance feedback retrieval. The vector approximation file (VA-File) approach overcomes some of the difficulties of high dimensional vector spaces, but cannot be applied to relevance feedback retrieval using kernel distances in the data measurement space. This paper introduces a novel KVA-File (kernel VA-File) that extends VA-File to kernel-based retrieval methods. A key observation is that kernel distances may be non-linear in the data measurement space but is still linear in an induced feature space. It is this linear invariance in the induced feature space that enables KVA-File to work with kernel distances. An efficient approach to approximating vectors in an induced feature space is presented with the corresponding upper and lower distance bounds. Thus an effective indexing method is provided for kernel-based relevance feedback image retrieval methods. Experimental results using large image data sets (approximately 100,000 images with 463 dimensions of measurement) validate the efficacy of our method.

Original language | English |
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Title of host publication | MMDB 2003 |

Subtitle of host publication | Proceedings of the First ACM International Workshop on Multimedia Databases |

Editors | S.-C. Chen, M.-L. Shyo |

Pages | 48-54 |

Number of pages | 7 |

State | Published - 1 Dec 2003 |

Event | MMDB 2003: Proceedings of the First ACM International Workshop on Multimedia Databases - New Orleans, LA, United States Duration: 7 Nov 2003 → 7 Nov 2003 |

### Publication series

Name | MMDB 2003: Proceedings of the First ACM International Workshop on Multimedia Databases |
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### Other

Other | MMDB 2003: Proceedings of the First ACM International Workshop on Multimedia Databases |
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Country | United States |

City | New Orleans, LA |

Period | 7/11/03 → 7/11/03 |

### Fingerprint

### Keywords

- Content-based image retrieval
- Flexible metrics
- Indexing
- Kernel methods
- Relevance feedback
- VA-Files

### Cite this

*MMDB 2003: Proceedings of the First ACM International Workshop on Multimedia Databases*(pp. 48-54). (MMDB 2003: Proceedings of the First ACM International Workshop on Multimedia Databases).

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*MMDB 2003: Proceedings of the First ACM International Workshop on Multimedia Databases.*MMDB 2003: Proceedings of the First ACM International Workshop on Multimedia Databases, pp. 48-54, MMDB 2003: Proceedings of the First ACM International Workshop on Multimedia Databases, New Orleans, LA, United States, 7/11/03.

**Kernel VA-files for relevance feedback retrieval.** / Heisterkamp, Douglas R.; Peng, Jing.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Kernel VA-files for relevance feedback retrieval

AU - Heisterkamp, Douglas R.

AU - Peng, Jing

PY - 2003/12/1

Y1 - 2003/12/1

N2 - Many data partitioning index methods perform poorly in high dimensional space and do not support relevance feedback retrieval. The vector approximation file (VA-File) approach overcomes some of the difficulties of high dimensional vector spaces, but cannot be applied to relevance feedback retrieval using kernel distances in the data measurement space. This paper introduces a novel KVA-File (kernel VA-File) that extends VA-File to kernel-based retrieval methods. A key observation is that kernel distances may be non-linear in the data measurement space but is still linear in an induced feature space. It is this linear invariance in the induced feature space that enables KVA-File to work with kernel distances. An efficient approach to approximating vectors in an induced feature space is presented with the corresponding upper and lower distance bounds. Thus an effective indexing method is provided for kernel-based relevance feedback image retrieval methods. Experimental results using large image data sets (approximately 100,000 images with 463 dimensions of measurement) validate the efficacy of our method.

AB - Many data partitioning index methods perform poorly in high dimensional space and do not support relevance feedback retrieval. The vector approximation file (VA-File) approach overcomes some of the difficulties of high dimensional vector spaces, but cannot be applied to relevance feedback retrieval using kernel distances in the data measurement space. This paper introduces a novel KVA-File (kernel VA-File) that extends VA-File to kernel-based retrieval methods. A key observation is that kernel distances may be non-linear in the data measurement space but is still linear in an induced feature space. It is this linear invariance in the induced feature space that enables KVA-File to work with kernel distances. An efficient approach to approximating vectors in an induced feature space is presented with the corresponding upper and lower distance bounds. Thus an effective indexing method is provided for kernel-based relevance feedback image retrieval methods. Experimental results using large image data sets (approximately 100,000 images with 463 dimensions of measurement) validate the efficacy of our method.

KW - Content-based image retrieval

KW - Flexible metrics

KW - Indexing

KW - Kernel methods

KW - Relevance feedback

KW - VA-Files

UR - http://www.scopus.com/inward/record.url?scp=13444307692&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:13444307692

SN - 1581137265

SN - 9781581137262

T3 - MMDB 2003: Proceedings of the First ACM International Workshop on Multimedia Databases

SP - 48

EP - 54

BT - MMDB 2003

A2 - Chen, S.-C.

A2 - Shyo, M.-L.

ER -