A Dynamic User Concept Pattern Learning Framework for Content-Based Image Retrieval  

Oleh:

Shu-Ching Chen ; Rubin, Stuart H. ; Mei ling Shyu

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Systems, Man, and Cybernetics: Part C Applications and Reviews vol. 36 no. 6 (Nov. 2006), page 772-783.
Topik: Content-Based Image Retrieval (CBIR); Multiple Intance; Neural Network

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II69.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

A rapid increase in the amount of image data and the inefficiency of traditional text-based image retrieval systems have served to make content-based image retrieval an active research field. It is crucial to effectively discover users' concept patterns through an acquired understanding of the subjective role played by humans in the retrieval process for such systems. A learning and retrieval framework is used to achieve this. It seamlessly incorporates multiple instance learning for relevant feedback to discover users concept patterns-especially in the region of greatest user interest. It also maps the local feature vector of that region to the high-level concept pattern. This underlying mapping can be progressively discovered through feedback and learning. The user guides the retrieval systems learning process using his/her focus of attention. Retrieval performance is tested to establish the feasibility and effectiveness of the proposed learning and retrieval framework.

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