Use this resource - and many more! - in your textbook!
AcademicPub holds over eight million pieces of educational content for you to mix-and-match your way.
Relevance feedback techniques for image retrieval using multiple attributes
1999 / IEEE / 0-7695-0253-9
This item was taken from the IEEE Conference ' Relevance feedback techniques for image retrieval using multiple attributes ' The paper proposes a relevance feedback (RF) approach to content based image retrieval using multiple attributes. The proposed approach has been applied to images' text and color attributes. In order to ensure that meaningful features are extracted, a pseudo object model based on color coherence vector has been adopted to model color content. The RF approach employs techniques developed in the fields of information retrieval and machine learning to extract pertinent features from each of the attributes. It then uses the user's relevance judgments to estimate the importance of different attributes in an integrated content based image retrieval. The system developed has been tested on a large image collection containing over 12000 images. The results demonstrate that the proposed RF approaches and pseudo object based color model are effective.
Image Colour Analysis
Learning (artificial Intelligence)
Very Large Databases
Pseudo Object Based Color Model
Relevance Feedback Techniques
Content Based Image Retrieval
Color Coherence Vector
Large Image Collection
Content Based Retrieval
Pseudo Object Model