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Similarity measure is a critical component in image retrieval systems, and learning similarity measure from the relevance feedback has become a promising way to enhance retrieval performance. Existing approaches mainly focus on learning the visual similarity measure from online feedbacks or constructing the semantic similarity measure depended on historical feedbacks log. However, there is still a...
In this paper, we propose a new learning method in human motion data analysis. We use Isomap algorithm to reduce high dimensionality of motion's features data. And Support Vector Machine (SVM) for clustering and handling new data. Then data driven decision trees based on multiple instance are automatically constructed to reflect the influence of each point during the comparison of motion similarity...
This paper presents a new algorithm based on boosting for interactive object retrieval in images. Recent works propose ”online boosting” algorithms where weak classifier sets are iteratively trained from data. These algorithms are proposed for visual tracking in videos, and are not well adapted to ”online boosting” for interactive retrieval. We propose in this paper to iteratively build weak classifiers...
The number of digital images that needs to be acquired, analyzed, classified, stored and retrieved in the medical centers is exponentially growing with the advances in medical imaging technology. Accordingly, medical image classification and retrieval has become a popular topic in the recent years. Despite many projects focusing on this problem, proposed solutions are still far from being sufficiently...
Relevance feedback (RF) is an importance technique in CBIR (Content-Based Image Retrieval) systems to bridge the semantic gap between low-level visual features (eg. color, shape, texture) and high-level human perception. One of the most frequently used methods to do RF is Support Vector Machine (SVM), which has a good generalization ability in pattern recognition. But when the training data is insufficient,...
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