A method, comprising: - extracting region proposals from each frame of a video, the video being associated with tags indicating objects appearing in the video; - determining a region-wise score for a region proposal, the region-wise score defining region proposals' likelihood to contain an object based on a binary map; - selecting a set of region proposals from the extracted region proposals according to the region-wise score - generating a first set of region proposals from the selected region proposals, wherein the first set of region proposals correspond to various objects appearing in the video; - identifying objects of interest indicated by the video tags by object classification by determining a confidence map for the first set of region proposals over the region-wise score of the first set of region proposals and a classification confidence score, wherein the classification confidence score has been determined for the first set of region proposals with respect to a specific class label associated with the video; characterized in that the method further comprises - generating a second set of region proposals by slicing the confidence map; - determining an object-specific representation by an iterative tracking method, the object-specific representation capturing a distribution of a same object instances in deep feature space, wherein the iterative tracking method comprises * tracking a selected region from the second set of region proposals from an earliest frame to a last frame; * tracking other region proposals from the second set of region proposals in order to eliminate region proposals overlapping with the selected region, whereupon the eliminated region proposals are used to form a tracked region proposal group; - determining a stability indicator for each tracked region proposal group by using a number of region proposals overlapping with the tracked region proposal, wherein said stability indicator indicates a stability of a tracked region proposal group; - sampling positive examples from the set of tracked region proposal groups obtained from the iterative tracking method based on the stability indicator; - training a linear support vector machine for each class with training class labels using the positive examples; - updating the confidence map using classification confidence scores produced using the linear support vector machine for each class; and - performing object segmentation using the updated confidence map as a cost in the segmentation formulated as a super-pixel labelling problem of assigning each superpixel with a label which represents different objects appearing in the video and background respectively. - extracting region proposals from each frame of a video, the video being associated with tags indicating objects appearing in the video; - determining a region-wise score for a region proposal, the region-wise score defining region proposals' likelihood to contain an object based on a binary map; - selecting a set of region proposals from the extracted region proposals according to the region-wise score - generating a first set of region proposals from the selected region proposals, wherein the first set of region proposals correspond to various objects appearing in the video; - identifying objects of interest indicated by the video tags by object classification by determining a confidence map for the first set of region proposals over the region-wise score of the first set of region proposals and a classification confidence score, wherein the classification confidence score has been determined for the first set of region proposals with respect to a specific class label associated with the video; - generating a second set of region proposals by slicing the confidence map; - determining an object-specific representation by an iterative tracking method, the object-specific representation capturing a distribution of a same object instances in deep feature space, wherein the iterative tracking method comprises * tracking a selected region from the second set of region proposals from an earliest frame to a last frame; * tracking other region proposals from the second set of region proposals in order to eliminate region proposals overlapping with the selected region, whereupon the eliminated region proposals are used to form a tracked region proposal group; * tracking a selected region from the second set of region proposals from an earliest frame to a last frame; * tracking other region proposals from the second set of region proposals in order to eliminate region proposals overlapping with the selected region, whereupon the eliminated region proposals are used to form a tracked region proposal group; - determining a stability indicator for each tracked region proposal group by using a number of region proposals overlapping with the tracked region proposal, wherein said stability indicator indicates a stability of a tracked region proposal group; - sampling positive examples from the set of tracked region proposal groups obtained from the iterative tracking method based on the stability indicator; - training a linear support vector machine for each class with training class labels using the positive examples; - updating the confidence map using classification confidence scores produced using the linear support vector machine for each class; and - performing object segmentation using the updated confidence map as a cost in the segmentation formulated as a super-pixel labelling problem of assigning each superpixel with a label which represents different objects appearing in the video and background respectively.