Abstract: A method of generating a training sample for matching unlabelled objects in a sequence of images. A first representation of unlabelled objects is generated from images of a first and second set of images. A second representation of the unlabelled objects is generated using an unsupervised method. An anchor image in the first set is selected. A first set of candidate images in the second set that are close to the anchor image in both the first and second representations, is determined. A second set of candidate images in the second set that are distant from the anchor image in either the first or the second representations, is determined. A match candidate image is selected from the first set or the second set of candidate images. The training sample is generated from at least the anchor image and the match candidate image.

Abstract: A method of performing person re-identification for images captured by at least two camera pairs operating with different environmental factors. Descriptors representing characteristics of objects corresponding to a person in the images are clustered. A probability distribution of the clustered descriptors is determined. A coupling map for the images is determined based on the probability distribution. A cross-correlation between at least two of the coupling maps is determined. A similarity of the images captured by the camera pairs is determined according to the cross-correlation. Person re-identification is performed for the different environmental factors using the descriptors, based on the determined similarity.

Abstract: A method of generating a training sample for matching unlabelled objects in a sequence of images. A first representation of unlabelled objects is generated from images of a first and second set of images. A second representation of the unlabelled objects is generated using an unsupervised method. An anchor image in the first set is selected. A first set of candidate images in the second set that are close to the anchor image in both the first and second representations, is determined. A second set of candidate images in the second set that are distant from the anchor image in either the first or the second representations, is determined. A match candidate image is selected from the first set or the second set of candidate images. The training sample is generated from at least the anchor image and the match candidate image.

Abstract: A method of performing person re-identification for images captured by at least two camera pairs operating with different environmental factors. Descriptors representing characteristics of objects corresponding to a person in the images are clustered. A probability distribution of the clustered descriptors is determined. A coupling map for the images is determined based on the probability distribution. A cross-correlation between at least two of the coupling maps is determined. A similarity of the images captured by the camera pairs is determined according to the cross-correlation. Person re-identification is performed for the different environmental factors using the descriptors, based on the determined similarity.

Abstract: A method of comparing objects in images. A dictionary determined from a plurality of feature vectors formed from a test image and codes formed by applying the dictionary to the feature vectors, is received. The dictionary is based on a modified manifold obtained by determining correspondences for codes using pairwise similarities between codes. Comparison codes are determined for the objects in the images by applying the dictionary to feature vectors of the objects in the images. The objects in the images are compared based on the comparison codes of the objects.

Abstract: A method of determining similarity of objects in images. Feature vectors are determined for objects in images captured by cameras operating in a training domain. Feature vectors are determined for the objects in images captured by cameras operating in a target domain, the cameras of the target domain operating with different environmental factors to the cameras of the training domain. A mapping is determined for a difference in the feature vectors of the training domain and the target domain. The difference in the feature vectors of the training domain and the target domain is converted to a matching space by applying the determined mapping to the feature vectors of the training domain and the target domain. A classifier is determined using data associated with the feature vectors of the training domain in the matching space.

Abstract: A method of comparing objects in images. A dictionary determined from a plurality of feature vectors formed from a test image and codes formed by applying the dictionary to the feature vectors is received, the dictionary being based on a difference in mean values between the codes. Comparison codes are determined for the objects in the images by applying the dictionary to feature vectors of the objects in the images. The objects in the images are compared based on the comparison codes of the objects.

Abstract: A computer-implementable method for determining a score for matching images includes extracting and determining. Features are extracted from each of a query image acquired in a first modality and a database image acquired in a second modality. A vector distance distribution is determined based on a codebook for the first modality for each of the query image and the database image represented by the extracted features. The method determines a set of distances between the vector distance distribution determined for the query image and the vector distance distribution determined for the database image. The score for matching the query image and the database image is determined based on the determined set of distances.

Abstract: A method of comparing objects in images. A dictionary determined from a plurality of feature vectors formed from a test image and codes formed by applying the dictionary to the feature vectors, is received. The dictionary is based on a modified manifold obtained by determining correspondences for codes using pairwise similarities between codes. Comparison codes are determined for the objects in the images by applying the dictionary to feature vectors of the objects in the images. The objects in the images are compared based on the comparison codes of the objects.

Abstract: A method of comparing objects in images. A dictionary determined from a plurality of feature vectors formed from a test image and codes formed by applying the dictionary to the feature vectors is received, the dictionary being based on a difference in mean values between the codes. Comparison codes are determined for the objects in the images by applying the dictionary to feature vectors of the objects in the images. The objects in the images are compared based on the comparison codes of the objects.

Abstract: A method of determining similarity of objects in images. Feature vectors are determined for objects in images captured by cameras operating in a training domain. Feature vectors are determined for the objects in images captured by cameras operating in a target domain, the cameras of the target domain operating with different environmental factors to the cameras of the training domain. A mapping is determined for a difference in the feature vectors of the training domain and the target domain. The difference in the feature vectors of the training domain and the target domain is converted to a matching space by applying the determined mapping to the feature vectors of the training domain and the target domain. A classifier is determined using data associated with the feature vectors of the training domain in the matching space.

Abstract: A method of matching a person in captured images comprises determining first feature vectors from a first image sequence of person(s), and determining second feature vectors from a second image sequence of person(s). The first and second feature vectors are determined based on properties of pixels located in the first and second image sequences respectively. The method further comprises, for a first feature vector corresponding to a first person in the first image sequence, determining a reference distance to one of the second feature vectors corresponding to a reference person in the second image sequence; determining a distance metric by constraining a distance between the first feature vector and a feature vector corresponding to the first person in the second image sequence, according to the determined reference distance; and matching a pair of images of the person in the captured images based on the distance metric.

Abstract: A computer-implementable method for determining a score for matching images includes extracting and determining. Features are extracted from each of a query image acquired in a first modality and a database image acquired in a second modality. A vector distance distribution is determined based on a codebook for the first modality for each of the query image and the database image represented by the extracted features. The method determines a set of distances between the vector distance distribution determined for the query image and the vector distance distribution determined for the database image. The score for matching the query image and the database image is determined based on the determined set of distances.

Abstract: Disclosed is a method of storing a scene model (230) used for foreground/background separation of a scene (223) captured by a camera (100), the scene model comprising a plurality of visual element models (240) each associated with a set (250) of mode models (260, 270, . . . ), said method comprising determining (330) if a change in the scene satisfies a pre-determined threshold (MFGT), creating (710) a background image based on a plurality of the mode models classified as background, matching (360) an input image (210) captured by the camera and the background image by determining a similarity score, and updating the set of mode models if the similarity score satisfies a pre-determined similarity threshold by creating at least one mode model based on the received input image, said mode model being classified as background.