Abstract: A method and system generate an ensemble image representation for cross-domain retrieval of a fashion item image from a database by using a three-stream Siamese triplet loss trained convolutional neural network to generate a first retrieval descriptor corresponding to an inputted query image; using an average precision loss trained convolutional neural network to generate a second retrieval descriptor corresponding to the inputted query image; concatenating both the first retrieval descriptor and the second retrieval descriptor; and I2-normalizing the concatenated result to generate the ensemble image representation. During a first stage of the method and system, database items are cropped using a trained fine-grained fashion item detector.

Abstract: A system and method that performs iterative foreground detection and multi-object segmentation in an image is disclosed herein. A new background prior is introduced to improve the foreground segmentation results. Three complimentary methods detect and segment foregrounds containing multiple objects. The first method performs an iterative segmentation of the image to pull out the salient objects in the image. In a second method, a higher dimensional embedding of the image graph is used to estimate the saliency score and extract multiple salient objects. A third method uses a metric to automatically pick the number of eigenvectors to consider in an alternative method to iteratively compute the image saliency map. Experimental results show that these methods succeed in accurately extracting multiple foreground objects from an image.

Abstract: A method and system generate an ensemble image representation for cross-domain retrieval of a fashion item image from a database by using a three-stream Siamese triplet loss trained convolutional neural network to generate a first retrieval descriptor corresponding to an inputted query image; using an average precision loss trained convolutional neural network to generate a second retrieval descriptor corresponding to the inputted query image; concatenating both the first retrieval descriptor and the second retrieval descriptor; and I2-normalizing the concatenated result to generate the ensemble image representation. During a first stage of the method and system, database items are cropped using a trained fine-grained fashion item detector.

Abstract: A system and method that performs iterative foreground detection and multi-object segmentation in an image is disclosed herein. A new background prior is introduced to improve the foreground segmentation results. Three complimentary methods detect and segment foregrounds containing multiple objects. The first method performs an iterative segmentation of the image to pull out the salient objects in the image. In a second method, a higher dimensional embedding of the image graph is used to estimate the saliency score and extract multiple salient objects. A third method uses a metric to automatically pick the number of eigenvectors to consider in an alternative method to iteratively compute the image saliency map. Experimental results show that these methods succeed in accurately extracting multiple foreground objects from an image.

Abstract: A system and method that performs iterative foreground detection and multi-object segmentation in an image is disclosed herein. A new background prior is introduced to improve the foreground segmentation results. Three complimentary methods detect and segment foregrounds containing multiple objects. The first method performs an iterative segmentation of the image to pull out the salient objects in the image. In a second method, a higher dimensional embedding of the image graph is used to estimate the saliency score and extract multiple salient objects. A third method uses a metric to automatically pick the number of eigenvectors to consider in an alternative method to iteratively compute the image saliency map. Experimental results show that these methods succeed in accurately extracting multiple foreground objects from an image.

Abstract: A system and method that performs iterative foreground detection and multi-object segmentation in an image is disclosed herein. A new background prior is introduced to improve the foreground segmentation results. Three complimentary methods detect and segment foregrounds containing multiple objects. The first method performs an iterative segmentation of the image to pull out the salient objects in the image. In a second method, a higher dimensional embedding of the image graph is used to estimate the saliency score and extract multiple salient objects. A third method uses a metric to automatically pick the number of eigenvectors to consider in an alternative method to iteratively compute the image saliency map. Experimental results show that these methods succeed in accurately extracting multiple foreground objects from an image.