24,334 research outputs found
Deep Boosting: Layered Feature Mining for General Image Classification
Constructing effective representations is a critical but challenging problem
in multimedia understanding. The traditional handcraft features often rely on
domain knowledge, limiting the performances of exiting methods. This paper
discusses a novel computational architecture for general image feature mining,
which assembles the primitive filters (i.e. Gabor wavelets) into compositional
features in a layer-wise manner. In each layer, we produce a number of base
classifiers (i.e. regression stumps) associated with the generated features,
and discover informative compositions by using the boosting algorithm. The
output compositional features of each layer are treated as the base components
to build up the next layer. Our framework is able to generate expressive image
representations while inducing very discriminate functions for image
classification. The experiments are conducted on several public datasets, and
we demonstrate superior performances over state-of-the-art approaches.Comment: 6 pages, 4 figures, ICME 201
Automatic human face detection for content-based image annotation
In this paper, an automatic human face detection approach using colour analysis is applied for content-based image annotation. In the face detection, the probable face region is detected by adaptive boosting algorithm, and then combined with a colour filtering classifier to enhance the accuracy in face detection. The initial experimental benchmark shows the proposed scheme can be efficiently applied for image annotation with higher fidelity
Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition
This paper presents a novel quadratic projection based feature extraction
framework, where a set of quadratic matrices is learned to distinguish each
class from all other classes. We formulate quadratic matrix learning (QML) as a
standard semidefinite programming (SDP) problem. However, the con- ventional
interior-point SDP solvers do not scale well to the problem of QML for
high-dimensional data. To solve the scalability of QML, we develop an efficient
algorithm, termed DualQML, based on the Lagrange duality theory, to extract
nonlinear features. To evaluate the feasibility and effectiveness of the
proposed framework, we conduct extensive experiments on biometric recognition.
Experimental results on three representative biometric recogni- tion tasks,
including face, palmprint, and ear recognition, demonstrate the superiority of
the DualQML-based feature extraction algorithm compared to the current
state-of-the-art algorithm
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