Incorporating Prior Knowledge into Multi-label Boosting for Cross-Modal Image Annotation and Retrieval

Automatic image annotation (AIA) has proved to be an effective and promising solution to automatically deduce the high-level semantics from low-level visual features. In this paper, we formulate the task of image annotation as a multi-label, multi class semantic image classification problem and prop...

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description Automatic image annotation (AIA) has proved to be an effective and promising solution to automatically deduce the high-level semantics from low-level visual features. In this paper, we formulate the task of image annotation as a multi-label, multi class semantic image classification problem and propose a simple yet effective joint classification framework in which probabilistic multi-label boosting and contextual semantic constraints are integrated seamlessly. We conducted experiments on a medium-sized image collection including about 5000 images from Corel Stock Photo CDs. The experimental results demonstrated that the annotation performance of our proposed method is comparable to state-of-the-art approaches, showing the effectiveness and feasibility of the proposed unified framework.
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subjects Annotation Model
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Confidence Score
Exact sciences and technology
Image Annotation
Information systems. Data bases
Latent Semantic Analysis
Memory organisation. Data processing
Pattern recognition. Digital image processing. Computational geometry
Semantic Label
Software
title Incorporating Prior Knowledge into Multi-label Boosting for Cross-Modal Image Annotation and Retrieval
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