Image feature space is typically complex due to the high dimensionality of data. Effective handling of this space has prompted many research efforts in the study of dimensionality reduction in the image domain. In this paper, we propose a semi-supervised reduction method that leverages relevance feedback information in the retrieval process to learn suitable linear and orthogonal embeddings. In the reduced space constructed by the proposed embedding, relevant images are kept close to each other, while irrelevant ones are dispersed far apart. The experimental results demonstrate the superiority of our method. Copyright 2008 ACM
Relevance feedback mechanisms are adopted to refine image-based queries by asking users to mark the ...
Relevance feedback has recently emerged as a solution to the problem of improving the retrieval perf...
Relevance feedback has recently emerged as a solution to the problem of providing an effective respo...
Image feature space is typically complex due to the high dimensionality of data. Effective handling ...
High-retrieval precision in content-based image retrieval can be attained by adopting relevance feed...
This paper proposes a novel semi-supervised dimensionality reduction learning algorithm for the rank...
Content-based image retrieval methods based on the Euclidean metric expect the feature space to be i...
Relevance feedback approaches have been established as an important tool for interactive search, ena...
Content-based image retrieval methods based on the Euclidean metric expect the feature space to be ...
Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces f...
The problem of nonlinear dimensionality reduction is considered. We focus on problems where prior in...
Substantial benets can be gained from eective Relevance Feedback techniques in content-based image r...
Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces f...
Abstract. Nonlinear dimensionality reduction (NLDR) techniques offer powerful data visualization sch...
The thesis investigates various machine learning approaches to reducing data dimensionality, and stu...
Relevance feedback mechanisms are adopted to refine image-based queries by asking users to mark the ...
Relevance feedback has recently emerged as a solution to the problem of improving the retrieval perf...
Relevance feedback has recently emerged as a solution to the problem of providing an effective respo...
Image feature space is typically complex due to the high dimensionality of data. Effective handling ...
High-retrieval precision in content-based image retrieval can be attained by adopting relevance feed...
This paper proposes a novel semi-supervised dimensionality reduction learning algorithm for the rank...
Content-based image retrieval methods based on the Euclidean metric expect the feature space to be i...
Relevance feedback approaches have been established as an important tool for interactive search, ena...
Content-based image retrieval methods based on the Euclidean metric expect the feature space to be ...
Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces f...
The problem of nonlinear dimensionality reduction is considered. We focus on problems where prior in...
Substantial benets can be gained from eective Relevance Feedback techniques in content-based image r...
Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces f...
Abstract. Nonlinear dimensionality reduction (NLDR) techniques offer powerful data visualization sch...
The thesis investigates various machine learning approaches to reducing data dimensionality, and stu...
Relevance feedback mechanisms are adopted to refine image-based queries by asking users to mark the ...
Relevance feedback has recently emerged as a solution to the problem of improving the retrieval perf...
Relevance feedback has recently emerged as a solution to the problem of providing an effective respo...
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