A learning state-space model for image retrieval

Cheng Chieh Chiang, Yi Ping Hung, Greg C Lee

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper proposes an approach based on a state-space model for learning the user concepts in image retrieval. We first design a scheme of region-based image representation based on concept units, which are integrated with different types of feature spaces and with different region scales of image segmentation. The design of the concept units aims at describing similar characteristics at a certain perspective among relevant images. We present the details of our proposed approach based on a state-space model for interactive image retrieval, including likelihood and transition models, and we also describe some experiments that show the efficacy of our proposed model. This work demonstrates the feasibility of using a state-space model to estimate the user intuition in image retrieval.

Original languageEnglish
Article number83526
JournalEurasip Journal on Advances in Signal Processing
Volume2007
DOIs
Publication statusPublished - 2007 Jul 6

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Image retrieval
Image segmentation
Experiments

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

A learning state-space model for image retrieval. / Chiang, Cheng Chieh; Hung, Yi Ping; Lee, Greg C.

In: Eurasip Journal on Advances in Signal Processing, Vol. 2007, 83526, 06.07.2007.

Research output: Contribution to journalArticle

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