Learning Representative Objects from Images Using Quadratic Optimization
Xiaonan Lu, Jia Li, James Z. Wang
The Pennsylvania State University, University Park, PA 16802
With the development of Content-Based Image Retrieval (CBIR) and ever
increasing computing power, there is a notable growing interest in automatic
learning from images. In this
paper, we introduce a quadratic optimization based learning technique to enable
computers to learn visual characteristics of a semantic concept from unlabeled images. In our work, images
are represented by regions extracted from segmentation. Given a group of
images conveying a semantic concept, we attempt to detect the region corresponding
to the concept in every image using quadratic optimization. To characterize
the visual properties
of the concept, the mean of the feature vectors each describing the
concept-associated region of an image is calculated and referred to as
the representative feature vector. We
apply the proposed learning technique to image classification and
object recognition applications
and provide experimental results.
Full Paper in Color
Xiaonan Lu, Jia Li and James Z. Wang, ``Learning Representative Objects from Images Using Quadratic Optimization,'' Proc. International Conference on Machine Intelligence, invited for a special session, pp. 730-737, Tozeur, Tunisia, ACIDCA, November 2005.
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June 30, 2005