𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Kernel-based distance metric learning for content-based image retrieval

✍ Scribed by Hong Chang; Dit-Yan Yeung

Book ID
108152088
Publisher
Elsevier Science
Year
2007
Tongue
English
Weight
736 KB
Volume
25
Category
Article
ISSN
0262-8856

No coin nor oath required. For personal study only.

πŸ“œ SIMILAR VOLUMES

Probabilistic Feature Relevance Learning
Probabilistic Feature Relevance Learning for Content-Based Image Retrieval
✍ Jing Peng; Bir Bhanu; Shan Qing πŸ“‚ Article πŸ“… 1999 πŸ› Elsevier Science 🌐 English βš– 695 KB

Most of the current image retrieval systems use "one-shot" queries to a database to retrieve similar images. Typically a Knearest neighbor kind of algorithm is used, where weights measuring feature importance along each input dimension remain fixed (or manually tweaked by the user), in the computati

Long-term learning in content-based imag
Long-term learning in content-based image retrieval
✍ Jing Li; Nigel M. Allinson πŸ“‚ Article πŸ“… 2008 πŸ› John Wiley and Sons 🌐 English βš– 476 KB

## Abstract In content‐based image retrieval, relevance feedback is an interactive process, which builds a bridge to connect users with the search engine. It leads to much improved retrieval performance by updating the query and the similarity measure according to a user's preference; and recently

Edge based features for content based im
Edge based features for content based image retrieval
✍ Minakshi Banerjee; Malay K. Kundu πŸ“‚ Article πŸ“… 2003 πŸ› Elsevier Science 🌐 English βš– 854 KB

The common problem in content based image retrieval (CBIR) is selection of features. Image characterization with lesser number of features involving lower computational cost is always desirable. Edge is a strong feature for characterizing an image. This paper presents a robust technique for extracti