Proceedings Article10.1145/2072298.2071931
Knowledge propagation in large image databases using neighborhood information
Michael E. Houle,Vincent Oria,Shin'ichi Satoh,Jichao Sun +3 more
- 28 Nov 2011
- pp 1033-1036
TL;DR: A neighbor-based influence propagation approach KProp is proposed which builds a voting model and propagates the knowledge associated to some objects to similar objects and shows that KProp can perform efficiently through matrix computations and achieve better performance with fewer labeled examples per object.
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Abstract: The aim of this paper is to reduce to a minimum the level of human intervention in the semantic annotation process of images. Ideally, only one copy of each object of interest would be labeled manually, and the labels would then be propagated automatically to all other occurrences of the objects in the database. To that end, we propose a neighbor-based influence propagation approach KProp which builds a voting model and propagates the knowledge associated to some objects to similar objects. We show that KProp can perform efficiently through matrix computations and achieve better performance with fewer labeled examples per object.
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Citations
•Posted Content
Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency
TL;DR: A new multi-view graph learning framework is proposed, which for the first time simultaneously and explicitly models multi- View consistency and multi-View inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-viewgraph as well as the unified graph that fuses the consistent parts can be iteratively learned.
Improving k -NN Graph Accuracy Using Local Intrinsic Dimensionality
Michael E. Houle,Vincent Oria,Arwa M. Wali,Arwa M. Wali +3 more
- 04 Oct 2017
TL;DR: This paper proposes NNWID-Descent, a similarity graph construction method that utilizes the NNF- Descent framework while integrating a new feature selection criterion, Support-Weighted Intrinsic Dimensionality, that estimates the contribution of each feature to the overall intrinsic dimensionality.
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Annotation propagation in image databases using similarity graphs
TL;DR: An influence propagation strategy, SW-KProp, that requires no human intervention beyond the initial labeling of a subset of the images and which enhances the quality of the similarity graph by selecting a reduced feature set for each prelabeled image and rebuilding its neighborhood.
12
Multi-View Graph Learning by Joint Modeling of Consistency and Inconsistency
TL;DR: In this article , a new multi-view graph learning framework is proposed, which simultaneously and explicitly models multiview consistency and inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single view graph as well as the unified graph that fuses the consistent parts can be iteratively learned.
Improving the quality of K-NN graphs through vector sparsification: application to image databases
TL;DR: NNF-Descent is shown to be able to increase the proportion of semantically-related images over unrelated images within the neighbor sets, and that the proposed method generalizes well for other types of data which are represented by high-dimensional feature vectors.
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