806 research outputs found
Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval
Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1) an SVM classifier is unstable on a small-sized training set, 2) SVM's optimal hyperplane may be biased when the positive feedback samples are much less than the negative feedback samples, and 3) overfitting happens because the number of feature dimensions is much higher than the size of the training set. In this paper, we develop a mechanism to overcome these problems. To address the first two problems, we propose an asymmetric bagging-based SVM (AB-SVM). For the third problem, we combine the random subspace method and SVM for relevance feedback, which is named random subspace SVM (RS-SVM). Finally, by integrating AB-SVM and RS-SVM, an asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve these three problems and further improve the relevance feedback performance
Dynamical variety of shapes in financial multifractality
The concept of multifractality offers a powerful formal tool to filter out
multitude of the most relevant characteristics of complex time series. The
related studies thus far presented in the scientific literature typically limit
themselves to evaluation of whether or not a time series is multifractal and
width of the resulting singularity spectrum is considered a measure of the
degree of complexity involved. However, the character of the complexity of time
series generated by the natural processes usually appears much more intricate
than such a bare statement can reflect. As an example, based on the long-term
records of S&P500 and NASDAQ - the two world leading stock market indices - the
present study shows that they indeed develop the multifractal features, but
these features evolve through a variety of shapes, most often strongly
asymmetric, whose changes typically are correlated with the historically most
significant events experienced by the world economy. Relating at the same time
the index multifractal singularity spectra to those of the component stocks
that form this index reflects the varying degree of correlations involved among
the stocks.Comment: 26 pages, 10 figure
Features for matching people in different views
There have been significant advances in the computer vision field during the last decade.
During this period, many methods have been developed that have been successful in solving
challenging problems including Face Detection, Object Recognition and 3D Scene Reconstruction.
The solutions developed by computer vision researchers have been widely
adopted and used in many real-life applications such as those faced in the medical and
security industry. Among the different branches of computer vision, Object Recognition
has been an area that has advanced rapidly in recent years. The successful introduction of
approaches such as feature extraction and description has been an important factor in the
growth of this area. In recent years, researchers have attempted to use these approaches
and apply them to other problems such as Content Based Image Retrieval and Tracking.
In this work, we present a novel system that finds correspondences between people seen in
different images. Unlike other approaches that rely on a video stream to track the movement
of people between images, here we present a feature-based approach where we locate a
target’s new location in an image, based only on its visual appearance.
Our proposed system comprises three steps. In the first step, a set of features is extracted
from the target’s appearance. A novel algorithm is developed that allows extraction of features
from a target that is particularly suitable to the modelling task. In the second step,
each feature is characterised using a combined colour and texture descriptor. Inclusion
of information relating to both colour and texture of a feature add to the descriptor’s distinctiveness.
Finally, the target’s appearance and pose is modelled as a collection of such
features and descriptors. This collection is then used as a template that allows us to search
for a similar combination of features in other images that correspond to the target’s new
location.
We have demonstrated the effectiveness of our system in locating a target’s new position in
an image, despite differences in viewpoint, scale or elapsed time between the images. The
characterisation of a target as a collection of features also allows our system to robustly
deal with the partial occlusion of the target
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