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This study presents a proof-of-concept study to assess the prediction of emotional perceptive competency and implicit affective preferences from each other. Predictions are made using linear regression, principal component analysis with linear regression, and support vector machines. Results point to a strong, bidirectional relationship between preference for emotional stimuli and affective competency...
In this paper, we present a simple and efficient way to add supervised information into Fisher vectors, which has become a popular image representation method for image classification and retrieval purposes in recent years. The basic idea of our approach is to improve the Fisher kernel in the training process by adding a discriminative label comparison matrix to it. The resulting new representations,...
We present the MDS feature learning framework, in which multidimensional scaling (MDS) is applied on high-level pair wise image distances to learn fixed-length vector representations of images. The aspects of the images that are captured by the learned features, which we call MDS features, completely depend on what kind of image distance measurement is employed. With properly selected semantics-sensitive...
A new feature descriptor is presented for object and scene recognition. The new approach, called CDIKP, uniquely combines the scale-invariant feature detection with a robust projection kernel technique to produce highly efficient feature representation. The produced feature descriptors are highly-compact in comparisons to the state-of-the-art, do not require any pretraining step, and show superior...
This paper proposes a general feature selection approach for real-time image matching systems. To demonstrate the idea??s effectiveness, we focus on the issue of rotational invariance. Most current image matching methods compute and align local image patches to a uniform dominant orientation, which are either too computationally expensive for real-time systems or insufficiently robust. In contrast...
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