28 research outputs found
Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications
This paper presents a novel pairwise constraint propagation approach by
decomposing the challenging constraint propagation problem into a set of
independent semi-supervised learning subproblems which can be solved in
quadratic time using label propagation based on k-nearest neighbor graphs.
Considering that this time cost is proportional to the number of all possible
pairwise constraints, our approach actually provides an efficient solution for
exhaustively propagating pairwise constraints throughout the entire dataset.
The resulting exhaustive set of propagated pairwise constraints are further
used to adjust the similarity matrix for constrained spectral clustering. Other
than the traditional constraint propagation on single-source data, our approach
is also extended to more challenging constraint propagation on multi-source
data where each pairwise constraint is defined over a pair of data points from
different sources. This multi-source constraint propagation has an important
application to cross-modal multimedia retrieval. Extensive results have shown
the superior performance of our approach.Comment: The short version of this paper appears as oral paper in ECCV 201
Content-Based Retrieval in Endomicroscopy: Toward an Efficient Smart Atlas for Clinical Diagnosis
International audienceIn this paper we present the first Content-Based Image Retrieval (CBIR) framework in the field of in vivo endomicroscopy, with applications ranging from training support to diagnosis support. We propose to adjust the standard Bag-of-Visual-Words method for the retrieval of endomicroscopic videos. Retrieval performance is evaluated both indirectly from a classification point-of-view, and directly with respect to a perceived similarity ground truth. The proposed method significantly outperforms, on two different endomicroscopy databases, several state-of-the-art methods in CBIR. With the aim of building a self-training simulator, we use retrieval results to estimate the interpretation difficulty experienced by the endoscopists. Finally, by incorporating clinical knowledge about perceived similarity and endomicroscopy semantics, we are able: 1) to learn an adequate visual similarity distance and 2) to build visual-word-based semantic signatures that extract, from low-level visual features, a higher-level clinical knowledge expressed in the endoscopist own language
Learning Relevance Restricted Boltzmann Machine for Unstructured Group Activity and Event Understanding
This work is jointly supported by National Basic Research Program of China (2012CB316300), National Natural
Science Foundation of China (61525306, 61573354, 61135002, 61420106015), and Strategic Priority Research Program of the CAS (XDB02070100)
How to Supervise Topic Models
Supervised topic models are important machine learning tools whichhave been widely used in computer vision as well as in other domains. However,there is a gap in the understanding of the supervision impact on the model. Inthis paper, we present a thorough analysis on the behaviour of supervised topicmodels using Supervised Latent Dirichlet Allocation (SLDA) and propose twofactorized supervised topic models, which factorize the topics into signal andnoise. Experimental results on both synthetic data and real-world data for computer vision tasks show that supervision need to be boosted to be effective andfactorized topic models are able to enhance the performance.QC 20141024</p