METHOD AND SYSTEM FOR DETERMINING OBJECTS DEPICTED IN IMAGES

Techniques are disclosed for identifying objects in images. In one embodiment, transfer learning is employed to build new classifiers on top of pre-trained machine learning models, such as pre-trained convolutional neural networks (CNNs), by re-training classification layers of the pre-trained machi...

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Bibliographic Details
Main Author LI, Saishi Frank
Format Patent
LanguageEnglish
Published 28.03.2019
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:Techniques are disclosed for identifying objects in images. In one embodiment, transfer learning is employed to build new classifiers on top of pre-trained machine learning models, such as pre-trained convolutional neural networks (CNNs), by re-training classification layers of the pre-trained machine learning models using new training data while keeping feature detection layers of the pre-trained machine learning models fixed. Subsequently, the re-trained machine learning models may take as input images depicting regions of interest extracted from larger images using a sliding window, a saliency map, an image disparity map, and/or a region of interest detection technique, and output classifications of objects in the input images. In addition, a meta model may be learned that aggregates outputs of the re-trained machine learning models for robustness.
Bibliography:Application Number: US201816143004