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Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification
Li-Jia Li · Hao Su · Eric Xing · Li Fei-Fei

Mon Dec 06 12:00 AM -- 12:00 AM (PST) @

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. In this paper, we propose a high-level image representation, called the Object Bank, 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 the Object Bank representation, superior performances on high level visual recognition tasks can be achieved with simple off-the-shelf classifiers such as logistic regression and linear SVM. Sparsity algorithms make our representation more efficient and scalable for large scene datasets, and reveal semantically meaningful feature patterns.

Author Information

Li-Jia Li (Stanford University)
Hao Su (UCSD)
Eric Xing (Petuum Inc. / Carnegie Mellon University)
Li Fei-Fei (Stanford University)

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