Motion-based classification of cartoons

Automatic Video Classification: A Survey of the Literature

“…Roach et al [42] extend their earlier work, classifying video using the audio features described in Roach and Mason [41] as well as visual features obtained in a manner similar to that described in Roach et al [75]. A GMM is used for classification of a linear combination of the conditional probabilities of the audio and visual features.…”

“…Truong et al [60] choose features they believe correspond to how humans identify genre. The features they use are average shot length, percentage of each type of shot transition (cut, fade, dissolve), camera movement, pixel luminance variance, rate of static scenes (i.e., little camera or object motion), [56] X X X Dimitrova et al [66] X X Truong et al [60] X X X X Kobla et al [7] X X Roach et al [75] X Roach et al [76] X X Pan and Faloutsos [77] X Lu et al [64] X Jadon et al [63] X X X Hauptmann et al [2] X X X Pan and Faloutsos [39] X Rasheed et al [62] X X Gibert et al [78] X X Yuan et al [65] X X X X Hong et al [79] X X X Brezeale and Cook [18] X Fischer et al [35] X X X X X Nam et al [4] X X X Huang et al [36] X X Qi et al [21] X Jasinschi and Louie [19] X X X X X Roach et al [42] X Rasheed and Shah [40] X X X Lin and Hauptmann [20] X Lee et al [37] X X Wang et al [13] X X X X Xu and Li [43] X X X Fan et al [8] X X X length of motion runs, standard deviation of a frame luminance histogram, percentage of pixels having brightness above some threshold, and percentage of pixels having saturation above some threshold. Classification is performed using the C4.5 decision tree to classify video into one of five classes: cartoon, commercial, music, news, or sports.…”

“…Roach et al [75] classify video as either cartoon or noncartoon. The visual feature chosen for classification is the motion of foreground objects, which are detected using pixelbased frame differencing.…”

“…Iyengar and Lippman [59] found that measuring motion using frame differencing produced results similar to those that measured motion using optical flow, yet frame differencing is simpler to implement and less computationally expensive. However, region-based features such as frame differencing are non-specific as to the direction of the motion [75].…”

Animated movie genre detection using symbolic fusion of text and image descriptors

“…A state-of-the art is available in [1]. [8] discusses an uni-modal (image) approach and testes the prospective potential of motion information to cartoon classification. However, results are obtained on a very limited data set (8 cartoon and 20 non cartoon sequences).…”