Generic object detection in images
AssigneeMicrosoft Technology Licensing, LLC
InventorsKaiming He, Jian Sun, Xiangyu Zhang
Life of this patent
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Abstract
Neural networks for object detection in images are used with a spatial pyramid pooling (SPP) layer. Using the SPP network structure, a fixed-length representation is generated regardless of image size and scale. The feature maps are computed from the entire image once, and the features are pooled in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. Thus, repeated computation of the convolutional features is avoided while accuracy is enhanced.
Claims20
A method to perform object detection in an image, the method comprising:
- receiving an input image;
- generating feature maps by one or more filters on a convolutional layer of a neural network processing the input image;
- spatially pooling responses of each filter at a spatial pyramid pooling (SPP) layer;
- providing outputs of the SPP layer to a fully-connected layer as fixed dimensional vectors;
- and
- training a classifier to detect one or more objects in the input image based on the fixed dimensional vectors received at the fully-connected layer.
A computing device to perform object detection in an image, the computing device comprising:
- an input module configured to receive an input image through one or more of a wired or wireless communication;
- a memory configured to store instructions;
- and
- a processor coupled to the memory and the input module, the processor executing an image processing application, wherein the image processing application is configured to:
- generate feature maps by employing one or more sliding filters on a convolutional layer of a neural network processing the input image;
- spatially pool responses of each filter in a plurality of spatial bins at a spatial pyramid pooling (SPP) layer;
- provide outputs of the SPP layer to a fully-connected layer as fixed dimensional vectors;
- and
- train a classifier to detect one or more objects in the input image based on the fixed dimensional vectors received at the fully-connected layer.
A computer-readable memory device with instructions stored thereon to perform object detection in an image, the instructions comprising:
- receiving an input image;
- generating feature maps by one or more filters on a convolutional layer of a first neural network processing the input image;
- extracting window-wise features from regions of deep convolutional feature maps;
- performing a selective search to generate a predefined number of candidate windows per image;
- spatially pooling responses of candidate windows at a spatial pyramid pooling (SPP) layer;
- providing outputs of the SPP layer to a fully-connected layer as fixed dimensional vectors;
- and
- training a classifier to detect one or more objects in the input image based on the fixed dimensional vectors received at the fully-connected layer.
Dependencies are inferred from the claim text.