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Unsupervised manifold learning using Reciprocal kNN Graphs in image re-ranking and rank aggregation tasks
TLDR
We propose a Reciprocal kNN Graph algorithm that considers the relationships among ranked lists in the context of a k-reciprocal neighborhood. Expand
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A scalable re-ranking method for content-based image retrieval
TLDR
In this paper, we present a novel approach for the re-ranking problem. Expand
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Image Re-ranking and Rank Aggregation Based on Similarity of Ranked Lists
TLDR
We present a novel approach for redefining distances and later reranking images aiming to improve the effectiveness of Content-Based Image Retrieval (CBIR) systems. Expand
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Image re-ranking and rank aggregation based on similarity of ranked lists
TLDR
We present a novel context-based approach for redefining distances and later re-ranking images aiming to improve the effectiveness of CBIR systems. Expand
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Shape Retrieval using Contour Features and Distance Optimization
TLDR
This paper presents a shape descriptor based on a set of features computed for each point of an object contour. Expand
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Unsupervised manifold learning through reciprocal kNN graph and Connected Components for image retrieval tasks
TLDR
This paper proposes a novel manifold learning approach that exploits the intrinsic dataset geometry for improving the effectiveness of image retrieval tasks. Expand
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Semi-supervised and active learning through Manifold Reciprocal kNN Graph for image retrieval
TLDR
In this paper, we discuss a novel semi-supervised learning algorithm for image retrieval tasks and model the correlation between features and label spaces for classification and retrieval. Expand
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Exploiting clustering approaches for image re-ranking
TLDR
This paper presents the Distance Optimization Algorithm (DOA), a re-ranking method aiming to improve the effectiveness of Content-Based Image Retrieval (CBIR) systems. Expand
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Unsupervised Distance Learning By Reciprocal kNN Distance for Image Retrieval
TLDR
This paper presents a novel unsupervised learning approach that takes into account the intrinsic dataset structure, which is represented in terms of the reciprocal neighborhood references found in different ranked lists. Expand
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Unsupervised Graph-based Rank Aggregation for Improved Retrieval
TLDR
We reformulate the ad-hoc retrieval problem as a document retrieval based on fusion graphs, which we propose as a new unified representation model capable of merging multiple ranks and expressing inter-relationships of retrieval results automatically. Expand
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