Abstract: An image salient object segmentation method and an apparatus based on reciprocal attention between a foreground and a background, and the method includes: obtaining a feature map corresponding to a training image based on a convolutional neural backbone network, and obtaining foreground and background initial feature responses according to the feature map; obtaining a reciprocal attention weight matrix, and updating the foreground and background initial feature responses according to the reciprocal attention weight matrix, to obtain foreground and background feature maps; training the convolutional neural backbone network according to the foreground and the background feature maps based on a cross entropy loss function and a cooperative loss function, to obtain a foreground and background segmentation convolutional neural network model; and inputting an image to be segmented into the foreground and background segmentation convolutional neural network model to obtain foreground and background prediction result

Abstract: An image salient object segmentation method and an apparatus based on reciprocal attention between a foreground and a background, and the method includes: obtaining a feature map corresponding to a training image based on a convolutional neural backbone network, and obtaining foreground and background initial feature responses according to the feature map; obtaining a reciprocal attention weight matrix, and updating the foreground and background initial feature responses according to the reciprocal attention weight matrix, to obtain foreground and background feature maps; training the convolutional neural backbone network according to the foreground and the background feature maps based on a cross entropy loss function and a cooperative loss function, to obtain a foreground and background segmentation convolutional neural network model; and inputting an image to be segmented into the foreground and background segmentation convolutional neural network model to obtain foreground and background prediction result

Abstract: Provided is a method for salient object segmentation of an image by aggregating a multi-linear exemplar regressors, including: analyzing and summarizing visual attributes and features of a salient object and a non-salient object using background prior and constructing a quadratic optimization problem, calculating an initial saliency probability map, selecting a most trusted foreground and a background seed point, performing manifold preserving foreground propagation, generating a final foreground probability map; generating a candidate object set for the image via an objectness adopting proposal, using a shape feature, a foregroundness and an attention feature to characterize each candidate object, training the linear exemplar regressors for each training image to characterize a particular saliency pattern of the image; aggregating a plurality of linear exemplar regressors, calculating saliency values for the candidate object set of a test image, and forming an image salient object segmentation model capable

Abstract: Provided is a method and a system for detecting and segmenting primary video objects with neighborhood reversibility, including: dividing each video frame of a video into super pixel blocks; representing each super pixel block with visual features; constructing and training a deep neural network to predict the initial foreground value for each super pixel block in the spatial domain; constructing a neighborhood reversible matrix and transmitting the initial foreground value, constructing an iterative optimization problem and resolving the final foreground value in the temporal spatial domain; performing pixel level transformation on the final foreground value; optimizing the final foreground value for the pixel using morphological smoothing operations; determining whether the pixel belongs to the primary video objects according to the final foreground value.

Abstract: Provided is a method and a system for detecting and segmenting primary video objects with neighborhood reversibility, including: dividing each video frame of a video into super pixel blocks; representing each super pixel block with visual features; constructing and training a deep neural network to predict the initial foreground value for each super pixel block in the spatial domain; constructing a neighborhood reversible matrix and transmitting the initial foreground value, constructing an iterative optimization problem and resolving the final foreground value in the temporal spatial domain; performing pixel level transformation on the final foreground value; optimizing the final foreground value for the pixel using morphological smoothing operations; determining whether the pixel belongs to the primary video objects according to the final foreground value.

Abstract: Provided is a method for salient object segmentation of an image by aggregating a multi-linear exemplar regressors, including: analyzing and summarizing visual attributes and features of a salient object and a non-salient object using background prior and constructing a quadratic optimization problem, calculating an initial saliency probability map, selecting a most trusted foreground and a background seed point, performing manifold preserving foreground propagation, generating a final foreground probability map; generating a candidate object set for the image via an objectness adopting proposal, using a shape feature, a foregroundness and an attention feature to characterize each candidate object, training the linear exemplar regressors for each training image to characterize a particular saliency pattern of the image; aggregating a plurality of linear exemplar regressors, calculating saliency values for the candidate object set of a test image, and forming an image salient object segmentation model capable

Abstract: The present invention provides a method for object segmentation in videos tagged with semantic labels, including: detecting each frame of a video sequence with an object bounding box detector from a given semantic category and an object contour detector, and obtaining a candidate object bounding box set and a candidate object contour set for each frame of the input video; building a joint assignment model for the candidate object bounding box set and the candidate object contour set and solving the model to obtain the initial object segment sequence; processing the initial object segment, to estimate a probability distribution of the object shapes; and optimizing the initial object segment sequence with a variant of graph cut algorithm that integrates the shape probability distribution, to obtain an optimal segment sequence.

Abstract: The present invention provides a method for object segmentation in videos tagged with semantic labels, including: detecting each frame of a video sequence with an object bounding box detector from a given semantic category and an object contour detector, and obtaining a candidate object bounding box set and a candidate object contour set for each frame of the input video; building a joint assignment model for the candidate object bounding box set and the candidate object contour set and solving the model to obtain the initial object segment sequence; processing the initial object segment, to estimate a probability distribution of the object shapes; and optimizing the initial object segment sequence with a variant of graph cut algorithm that integrates the shape probability distribution, to obtain an optimal segment sequence.