A method (300; 600), performed by a computing device (200; 810; 910), for generating training image data for a machine learning-based object recognition system, the method comprising: receiving generic image data of an object type; receiving (320; 602) recorded image data related to the object type, wherein the recorded image data comprises more than one recorded image data set, each recorded image data set comprising data corresponding to a recorded image related to the object type; modifying (330; 400; 622) the generic image data with respect to at least one imaging-related parameter (404-426), wherein the modified generic image data comprises more than one modified image data set, each modified image data set comprising data corresponding to a generated image of the object type; characterized in determining (340; 604-618, 624-640) a degree of similarity (D B ) between the modified generic image data and the recorded image data , wherein the determining a degree of similarity comprises sequentially rolling out image data sets so as to form vectors, and gathering the vectors to form matrices, subjecting these matrices to a kernel function, thus producing kernel matrices, computing eigenvectors of the kernel matrices, projecting image data set vectors obtained by sequentially rolling out image data sets onto respective sets of eigenvectors, and collecting resultant projection coefficients in corresponding projection matrices, and determining a degree of similarity between the resultant projection matrices; and when the determined degree of similarity (D B ) fulfills a similarity condition , storing (350; 644) the entirety of images of the modified generic image data as generated training image data of the object type. receiving generic image data of an object type; receiving (320; 602) recorded image data related to the object type, wherein the recorded image data comprises more than one recorded image data set, each recorded image data set comprising data corresponding to a recorded image related to the object type; modifying (330; 400; 622) the generic image data with respect to at least one imaging-related parameter (404-426), wherein the modified generic image data comprises more than one modified image data set, each modified image data set comprising data corresponding to a generated image of the object type; characterized in determining (340; 604-618, 624-640) a degree of similarity (D B ) between the modified generic image data and the recorded image data , wherein the determining a degree of similarity comprises sequentially rolling out image data sets so as to form vectors, and gathering the vectors to form matrices, subjecting these matrices to a kernel function, thus producing kernel matrices, computing eigenvectors of the kernel matrices, projecting image data set vectors obtained by sequentially rolling out image data sets onto respective sets of eigenvectors, and collecting resultant projection coefficients in corresponding projection matrices, and determining a degree of similarity between the resultant projection matrices; and when the determined degree of similarity (D B ) fulfills a similarity condition , storing (350; 644) the entirety of images of the modified generic image data as generated training image data of the object type. <EOS>