2 research outputs found
Optimizing Top Precision Performance Measure of Content-Based Image Retrieval by Learning Similarity Function
In this paper we study the problem of content-based image retrieval. In this
problem, the most popular performance measure is the top precision measure, and
the most important component of a retrieval system is the similarity function
used to compare a query image against a database image. However, up to now,
there is no existing similarity learning method proposed to optimize the top
precision measure. To fill this gap, in this paper, we propose a novel
similarity learning method to maximize the top precision measure. We model this
problem as a minimization problem with an objective function as the combination
of the losses of the relevant images ranked behind the top-ranked irrelevant
image, and the squared Frobenius norm of the similarity function parameter.
This minimization problem is solved as a quadratic programming problem. The
experiments over two benchmark data sets show the advantages of the proposed
method over other similarity learning methods when the top precision is used as
the performance measure.Comment: Pattern Recognition (ICPR), 2016 23st International Conference o
Semi-supervised structured output prediction by local linear regression and sub-gradient descent
We propose a novel semi-supervised structured output prediction method based
on local linear regression in this paper. The existing semi-supervise
structured output prediction methods learn a global predictor for all the data
points in a data set, which ignores the differences of local distributions of
the data set, and the effects to the structured output prediction. To solve
this problem, we propose to learn the missing structured outputs and local
predictors for neighborhoods of different data points jointly. Using the local
linear regression strategy, in the neighborhood of each data point, we propose
to learn a local linear predictor by minimizing both the complexity of the
predictor and the upper bound of the structured prediction loss. The
minimization problem is solved by sub-gradient descent algorithms. We conduct
experiments over two benchmark data sets, and the results show the advantages
of the proposed method.Comment: arXiv admin note: substantial text overlap with arXiv:1604.0301