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» Textural Features and Relevance Feedback for Image Retrieval
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VDB
1998
117views Database» more  VDB 1998»
13 years 5 months ago
Textural Features and Relevance Feedback for Image Retrieval
This paper focuses on the retrieval of complex images based on their textural content. We use GMRF for texture discrimination and a region-growing algorithm for texture segmentati...
Eugenio Di Sciascio, Giacomo Piscitelli, Augusto C...
ICIP
2002
IEEE
14 years 5 months ago
A feature re-weighting approach for relevance feedback in image retrieval
Users of image databases often prefer to retrieve relevant images by categories. Unfortunately, images are usually indexed by low-level features like color, texture and shape, whi...
Yimin Wu, Aidong Zhang
ESWA
2008
127views more  ESWA 2008»
13 years 4 months ago
A two-level relevance feedback mechanism for image retrieval
Content-based image retrieval (CBIR) is a group of techniques that analyzes the visual features (such as color, shape, texture) of an example image or image subregion to find simi...
Pei-Cheng Cheng, Been-Chian Chien, Hao-Ren Ke, Wei...
ICMCS
2000
IEEE
142views Multimedia» more  ICMCS 2000»
13 years 8 months ago
Incorporate Discriminant Analysis with EM Algorithm in Image Retrieval
One of the difficulties of Content-Based Image Retrieval (CBIR) is the gap between high-level concepts and low-level image features, e.g., color and texture. Relevance feedback wa...
Qi Tian, Ying Wu, Thomas S. Huang
CIVR
2005
Springer
107views Image Analysis» more  CIVR 2005»
13 years 9 months ago
Region Filtering Using Color and Texture Features for Image Retrieval
This paper presents a region-based image retrieval (RBIR) system in which users can choose specific regions as the query. Our goal is to assist the user to formulate more precise q...
Cheng-Chieh Chiang, Ming-Han Hsieh, Yi-Ping Hung, ...