Automatic TV program genre classification based on audio patterns

Automatic Video Classification: A Survey of the Literature

“…To reduce the dimensionality, stop lists and stemming are often applied prior to constructing a term feature vector. A TABLE I PAPERS UTILIZING TEXT-BASED APPROACHES Paper Closed Captions Speech Recognition OCR Brezeale and Cook [18] X Jasinschi and Louie [19] X Lin and Hauptmann [20] X Qi et al [21] X Wang et al [13] X Zhu et al [6] X stop list is a set of common words such as 'and' and 'the' [23]. Such words are unlikely to have much distinguishing power and are therefore removed from the master list of words prior to constructing the term feature vectors.…”

“…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.…”

“…Jasinschi and Louie [19] classify TV shows using audio features. Twenty audio features are extracted from 20 ms windows that overlap each other by 10 ms.…”

Automated Movie Genre Classification with LDA-based Topic Modeling

“…In [4], the authors evaluated a mel-frequency cepstral coefficients (MFCC) and neural network system for VGI and produced a correct classification rate of 51% in a 5-genre task. Additionally, Jasinschi et al [5] used sets of relative probabilities and mid-level audio categories to achieve automated classification of TV program genres at a precision of 65.2% and a recall of 89.2%.…”

Audio-Textual Classification System Design for Arabic News Videos

“…Support vector machine classifier is used to classify the news stories. Jasinschi and Louie [27] classify TV shows using audio, visual and textual features. The audio features are used to classify six categories; noise, speech, music, speech and noise, speech and speech, speech and music.…”