Framework for image thumbnailing based on visual similarity
First Claim
1. A method for detecting a region of interest in an image comprising:
- for each image in a dataset of images for which a region of interest has been established respectively, storing a dataset image representation based on features extracted from the image;
for an original image for which a region of interest is to be detected;
generating an original image representation for the original image based on features extracted from the image;
identifying a subset of similar images from the images in the dataset, the identified subset being based on a measure of similarity between the original image representation and respective dataset image representations;
training a classifier with information extracted from the established regions of interest of the subset of similar images, the information for training the classifier comprising at least one of;
positive examples, comprising information extracted from patches of the identified similar images that are within the established region of interest, andnegative examples comprising information extracted from patches of the identified similar images that are not within the established region of interest; and
with the trained classifier, identifying a region of interest in the original image.
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Abstract
An apparatus and method for detecting a region of interest in an image are disclosed. Image representations for a set of images that have been manually annotated with regions of interest are stored, along with positive and negative representations of each image which are similarly derived to the image representations except that they are based on features extracted from patches within the region of interest and outside it, respectively. For an original image for which a region of interest is desired, the stored information for K similar images is automatically retrieved and used to train a classifier. The trained classifier provides, for each patch of the original image, a probability of being in a region of interest, based extracted features of the patch (represented, for example, as a Fisher vector), which can be used to determine a region of interest in the original image.
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Citations
25 Claims
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1. A method for detecting a region of interest in an image comprising:
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for each image in a dataset of images for which a region of interest has been established respectively, storing a dataset image representation based on features extracted from the image; for an original image for which a region of interest is to be detected; generating an original image representation for the original image based on features extracted from the image; identifying a subset of similar images from the images in the dataset, the identified subset being based on a measure of similarity between the original image representation and respective dataset image representations; training a classifier with information extracted from the established regions of interest of the subset of similar images, the information for training the classifier comprising at least one of; positive examples, comprising information extracted from patches of the identified similar images that are within the established region of interest, and negative examples comprising information extracted from patches of the identified similar images that are not within the established region of interest; and with the trained classifier, identifying a region of interest in the original image. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 21, 22)
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14. A method for detecting a region of interest in an image comprising:
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for each image in a dataset of images for which a region of interest has been established respectively, storing a dataset image representation based on features extracted from the image, each dataset image representation beings based on features of patches extracted from the dataset image; for an original image for which a region of interest is to be detected; generating an original image representation for the original image based on features extracted from the image, the original image representation being based on features of patches extracted from the original image; identifying a subset of similar images from the images in the dataset, the identified subset being based on a measure of similarity between the original image representation and respective dataset image representations; training a classifier with information extracted from the established regions of interest of the subset of similar images; with the trained classifier, identifying a region of interest in the original image; wherein for each patch, a vector is generated, based on the extracted features, the vector comprising a Fisher vector. - View Dependent Claims (15, 16, 17, 18)
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19. A method for detecting a region of interest in an image comprising:
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for each image in a dataset of images for which a region of interest has been established respectively, storing a dataset image representation based on features extracted from the image, wherein, for each of a plurality of the images in the dataset of images, a semantic label is associated with the established region of interest, the semantic label being selected from a set of semantic labels, each relating to a different context; and for an original image for which a region of interest is to be detected; generating an original image representation for the original image based on features extracted from the image; identifying a subset of similar images from the images in the dataset, the identified subset being based on a measure of similarity between the original image representation and respective dataset image representations, and where a concept is specified, the identifying of the subset of similar images from the images in the dataset considers the semantic labels of the images in selecting the subset of similar images; training a classifier with information extracted from the established regions of interest of the subset of similar images; and with the trained classifier, identifying a region of interest in the original image.
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20. A method for detecting a region of interest in an image comprising:
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for each image in a dataset of images for which a region of interest has been established respectively, storing a dataset image representation based on features extracted from the image; for an original image for which a region of interest is to be detected; generating an original image representation for the original image based on features extracted from the image; identifying a subset of similar images from the images in the dataset, the identified subset being based on a measure of similarity between the original image representation and respective dataset image representations; training a classifier with information extracted from the established regions of interest of the subset of similar images; and with the trained classifier, identifying a region of interest in the original image, comprising; outputting a saliency map from the classifier in which patches of the image at multiple scales are each assigned a saliency value; partitioning the image into a set of overlapping windows; assigning each window a saliency score based on the saliency values of patches within the window; assigning each pixel of the image a saliency value based on the saliency values of windows in which the pixel is located.
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23. An apparatus for detecting a region of interest in an image comprising:
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memory which, for a dataset of images for which a respective region of interest has been established, stores a set of dataset image representations, each dataset image representation being derived from features extracted from a respective one of the images in the dataset; memory which stores instructions which, for an original image for which a region of interest is to be detected; generate an original image representation for the original image based on features extracted from the original image; identify a subset of similar images in the dataset, based on a measure of similarity between the original image representation and each dataset image representation; train a classifier to identify a region of interest in the original image, the classifier being trained with positive and negative examples, each of the positive examples comprising a high level representation based on features extracted from the established region of interest of a respective one of the subset of similar images and each of the negative examples comprising a high level representation based on features extracted from outside the established region of interest of a respective one of the subset of similar images.
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24. A method for detecting a region of interest in an image comprising:
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storing a set of image representations, each image representation being based on features extracted from patches of a dataset image, where for each dataset image, the patch features are identified as salient or non-salient based on whether or not the patch is within a manually identified region of interest; and for an original image for which a region of interest is to be detected; generating an original image representation for the original image based on features extracted from patches of the image; computing a distance measure between the original image representation and image representations in the set of image representations to identify a subset of similar image representations from the set of image representations; training a classifier with positive and negative examples extracted from the images corresponding to subset of similar image representations, the positive examples each being based on the salient patch features of a respective image and the negative examples being based on non-salient patch features of the respective image; and with the trained classifier, identifying a region of interest in the original image based on the patch features of the original image. - View Dependent Claims (25)
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Specification