Per-Sample Multiple Kernel Approach for Visual Concept Learning
نویسندگان
چکیده
منابع مشابه
Sample-Adaptive Multiple Kernel Learning
Existing multiple kernel learning (MKL) algorithms indiscriminately apply a same set of kernel combination weights to all samples. However, the utility of base kernels could vary across samples and a base kernel useful for one sample could become noisy for another. In this case, rigidly applying a same set of kernel combination weights could adversely affect the learning performance. To improve...
متن کاملVisual Comparison of Images Using Multiple Kernel Learning for Ranking
Ranking is the central problem for many applications such as web search, recommendation systems, and visual comparison of images. In this paper, the multiple kernel learning framework is generalized for the learning to rank problem. This approach extends the existing learning to rank algorithms by considering multiple kernel learning and consequently improves their effectiveness. The proposed a...
متن کاملLocalized Multiple Kernel Learning - A Convex Approach
We propose a localized approach to multiple kernel learning that can be formulated as a convex optimization problem over a given cluster structure. For which we obtain generalization error guarantees and derive an optimization algorithm based on the Fenchel dual representation. Experiments on real-world datasets from the application domains of computational biology and computer vision show that...
متن کاملInsights from Classifying Visual Concepts with Multiple Kernel Learning
Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL) techniques allow to determine an optimal linear combination of such similarity matrices. Classical approaches to MKL promote sparse mixtures. ...
متن کاملVisual saliency estimation by integrating features using multiple kernel learning
In the last few decades, significant achievements have been attained in predicting where humans look at images through different computational models. However, how to determine contributions of different visual features to overall saliency still remains an open problem. To overcome this issue, a recent class of models formulates saliency estimation as a supervised learning problem and according...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: EURASIP Journal on Image and Video Processing
سال: 2010
ISSN: 1687-5176,1687-5281
DOI: 10.1155/2010/461450