| 摘要 |
Robust low-level image features have been proven to be effective representations for a variety of visual recognition tasks such as object recognition and scene classification; but pixels, or even local image patches, carry little semantic meanings. For high-level visual tasks, such low-level image representations are potentially not enough. The present invention provides a high-level image representation where an image is represented as a scale-invariant response map of a large number of pre-trained generic object detectors, blind to the testing dataset or visual task. Leveraging on this representation, superior performances on high-level visual recognition tasks are achieved with relatively classifiers such as logistic regression and linear SVM classifiers.
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