# Automatic TV Logo Identification for Advertisement Detection without Prior Data
## 2 Literature Review  
According to the literature, detection of advertisement segments in television can be classified in two main classes: (1) detecting relevant features that are exclusive to advertisement or programming content, and (2) focused on the premise that an advertisement repeats multiple times through a broadcast. In the literature, these two methodologies are typically referred to as Feature-based and Repetition-based [2; 3], respectively. The proposal of this paper aligns with the former, but differs by not making use of prior data regarding the channel, the stream (e.g., content at the start or at the end), or the corresponding content. An early approach that incorporated both feature and repetition based methods was presented in [2]. This method incorporates two different detectors: The first detector analyses visual features such as black frame rate, motion, hard cuts, and fade rates from the advertisements; this process serves as an initial filter to find candidate advertisements. Then, a fingerprinting analysis is performed on the candidates to verify if the advertisement candidate can be found on an input database. This methodology is essentially based on the existence of black frames as delimiters to advertisement segments. Consequently, it failed to correctly detect advertisements in cases where black frames were not used. To tackle this problem, approaches based on detection of silences [4; 5] were used for a joint analysis of video and audio data, but these also failed to detect advertisements if the broadcaster did not use black frames nor silences to delimit the ads. Repetition-based methods generally require the existence of a database where information about the advertisements is stored. Sequence matching procedures are then applied to input streams in order to search the database and identify advertisements. The database must generally be initially provided or built through the analysis of streams. In [6], a multi-modal approach for detecting advertisements in repurposed videos is presented. It uses a repetition-based method that employs an analysis of audio and video features to determine if a given segment is repeated and check if it is inside a database. The database is first populated with advertisements obtained from identifying repeated segments in monitored streams, and it is automatically updated by adding to the database new repeating segments. Presented results indicated that this technique can reliably detect advertisements. However, the requirement for an initial dataset of known and used advertisements impairs its usage in many scenarios. In [7], a system that identifies repeating content on streams through the exploitation of dimension reduction techniques applied to audio is presented. This system assumes no prior knowledge, as it learns how to identify and detect the repeating content. Other similar methods have been proposed [8; 9], but a common limitation is that advertisements that appear a single time in a stream will not be detected. Recent approaches based on near duplicate video retrieval, such as that in [10], may also be applied to advertisement detection. However, again they suffer from the aforementioned problems. Looking more closely to feature-based advertisement detection methods, the channel logo has been considered a discriminative feature and is widely used. This happened under the premise that television broadcasters exclusively transmit their respective logo when programming content is airing, removing or washing it out when advertisements are displayed. In [11], an edge map-based logo detector is presented. It obtains logo candidates by analyzing gradient images and processes them using a pre-trained SVM, which validates if the edge map belongs to a known logo. The results reported were promising, but the requirement for a pre-trained SVM that knows all possible logos is a problem when applying this method to unconstrained scenarios. Another method for detecting logos was presented in [12].