IMAGE CROPPING USING ANCHOR SHAPES
In some embodiments, an image is received. The method includes analyzing the image based on a plurality of anchor shapes to generate respective outputs for anchor shapes in the plurality of anchor shapes. The output rates a cropping of the image using a respective anchor shape. The method analyzes respective outputs for the anchor shapes in the plurality of anchor shapes to select an anchor shape. The image is cropped using the anchor shape.
1 . A method comprising:
receiving an image;
analyzing the image based on a plurality of anchor shapes to generate respective outputs for anchor shapes in the plurality of anchor shapes, wherein the output rates a cropping of the image using a respective anchor shape;
analyzing respective outputs for the anchor shapes in the plurality of anchor shapes to select an anchor shape; and
cropping the image using the anchor shape.
2 . The method of claim 1 , wherein anchor shapes in the plurality of anchor shapes crop different portions of the image.
3 . The method of claim 1 , wherein anchor shapes in the plurality of anchor shapes are predefined shapes.
4 . The method of claim 1 , wherein analyzing the image comprises:
generating a feature map from the image, wherein the feature map represents one or more characteristics of the image; and
analyzing the feature map to generate respective outputs for the anchor shapes.
5 . The method of claim 4 , wherein:
the feature map comprises multiple channels, wherein channels are associated with characteristics of the image, and
the channels are analyzed to generate the output.
6 . The method of claim 1 , wherein analyzing the image comprises:
analyzing the image using a plurality of prediction networks, wherein prediction networks in the plurality of prediction networks are associated with respective anchor shapes in the plurality of anchor shapes.
7 . The method of claim 6 , wherein each prediction network is associated with an anchor shape in the plurality of anchor shapes and generates an output based on the respective anchor shape.
8 . The method of claim 6 , wherein each prediction network analyzes information from the image based on the respective anchor shape to generate the output.
9 . The method of claim 6 , wherein each prediction network analyzes information that is within the respective anchor shape and not outside of the respective anchor shape to generate the output.
10 . The method of claim 9 , wherein the information comprises a portion of a feature map that represents one or more characteristics of the image.
11 . The method of claim 1 , wherein the output represents a score for an overlap of a respective anchor shape and a preferred cropped image.
12 . The method of claim 1 , further comprising:
analyzing the image based on the plurality of anchor shapes to generate offset coordinates for anchor shapes in the plurality of anchor shapes, wherein the offset coordinates are used to crop the image.
13 . The method of claim 12 , wherein:
the offset coordinates adjust coordinates of the anchor shape to generate adjusted coordinates, and
the adjusted coordinates are used to crop the image.
14 . The method of claim 1 , further comprising:
training a model for the plurality of anchor shapes using a training image, wherein parameters of the model are adjusted using a comparison of a first output for an anchor shape to a second output that is based on a labeled shape for the training image.
15 . The method of claim 14 , wherein training comprises:
receiving the training image and the labeled shape;
generating the first output using the model for the anchor shape;
determining the second output, wherein the second output is based on an overlap of the labeled shape and the anchor shape; and
comparing the first output and the second output, wherein a difference between the first output and the second output is used to adjust the parameters of the model.
16 . The method of claim 15 , wherein training comprises:
generating first outputs using the model for anchor shapes in the plurality of anchor shapes;
determining second outputs, wherein the second outputs are based on an overlap of the labeled shape and the respective anchor shapes; and
comparing the respective first outputs and the respective second outputs, wherein a difference between the respective first outputs and the respective second outputs is used to adjust the parameters of the model for the anchor shapes.
17 . The method of claim 1 , wherein:
the anchor shape is a shape that is defined by coordinates, and
the coordinates are used to crop the image.
18 . A non-transitory computer-readable storage medium having stored thereon computer executable instructions, which when executed by a computing device, cause the computing device to be operable for:
receiving an image;
analyzing the image based on a plurality of anchor shapes to generate respective outputs for anchor shapes in the plurality of anchor shapes, wherein the output rates a cropping of the image using a respective anchor shape;
analyzing respective outputs for the anchor shapes in the plurality of anchor shapes to select an anchor shape; and
cropping the image using the anchor shape.
19 . The non-transitory computer-readable storage medium of claim 18 , wherein anchor shapes in the plurality of anchor shapes crop different portions of the image.
20 . An apparatus comprising:
one or more computer processors; and
a computer-readable storage medium comprising instructions for controlling the one or more computer processors to be operable for:
receiving an image;
analyzing the image based on a plurality of anchor shapes to generate respective outputs for anchor shapes in the plurality of anchor shapes, wherein the output rates a cropping of the image using a respective anchor shape;
analyzing respective outputs for the anchor shapes in the plurality of anchor shapes to select an anchor shape; and
cropping the image using the anchor shape.