3,054 research outputs found
Evaluating Content-centric vs User-centric Ad Affect Recognition
Despite the fact that advertisements (ads) often include strongly emotional
content, very little work has been devoted to affect recognition (AR) from ads.
This work explicitly compares content-centric and user-centric ad AR
methodologies, and evaluates the impact of enhanced AR on computational
advertising via a user study. Specifically, we (1) compile an affective ad
dataset capable of evoking coherent emotions across users; (2) explore the
efficacy of content-centric convolutional neural network (CNN) features for
encoding emotions, and show that CNN features outperform low-level emotion
descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram
(EEG) responses acquired from eleven viewers, and find that EEG signals encode
emotional information better than content descriptors; (4) investigate the
relationship between objective AR and subjective viewer experience while
watching an ad-embedded online video stream based on a study involving 12
users. To our knowledge, this is the first work to (a) expressly compare user
vs content-centered AR for ads, and (b) study the relationship between modeling
of ad emotions and its impact on a real-life advertising application.Comment: Accepted at the ACM International Conference on Multimodal Interation
(ICMI) 201
Affect Recognition in Ads with Application to Computational Advertising
Advertisements (ads) often include strongly emotional content to leave a
lasting impression on the viewer. This work (i) compiles an affective ad
dataset capable of evoking coherent emotions across users, as determined from
the affective opinions of five experts and 14 annotators; (ii) explores the
efficacy of convolutional neural network (CNN) features for encoding emotions,
and observes that CNN features outperform low-level audio-visual emotion
descriptors upon extensive experimentation; and (iii) demonstrates how enhanced
affect prediction facilitates computational advertising, and leads to better
viewing experience while watching an online video stream embedded with ads
based on a study involving 17 users. We model ad emotions based on subjective
human opinions as well as objective multimodal features, and show how
effectively modeling ad emotions can positively impact a real-life application.Comment: Accepted at the ACM International Conference on Multimedia (ACM MM)
201
An audio-based sports video segmentation and event detection algorithm
In this paper, we present an audio-based event detection algorithm shown to be effective when applied to Soccer video. The main benefit of this approach is the ability to recognise patterns that display high levels of crowd response correlated to key events. The soundtrack from a Soccer sequence is first parameterised using Mel-frequency Cepstral coefficients. It is then segmented into homogenous components using a windowing algorithm with a decision process based on Bayesian model selection. This decision process eliminated the need for defining a heuristic set of rules for segmentation. Each audio segment is then labelled using a series of Hidden Markov model (HMM) classifiers, each a representation of one of 6 predefined semantic content classes found in Soccer video. Exciting events are identified as those segments belonging to a crowd cheering class. Experimentation indicated that the algorithm was more effective for classifying crowd response when compared to traditional model-based segmentation and classification techniques
Identifying Suitable Slots for In-stream Video Advertisements
Video streaming services insert in-stream video ads at various points when providing users video content. However, such ad insertion can sometimes affect the viewing experience of the user if the content is paused at inappropriate moments or by unnecessarily prolonging the overall viewing session. This disclosure describes techniques to analyze video content using machine learning models or other suitable techniques to identify suitable portions that can be replaced by inserted ads or can be displayed together with an ad. By replacing sections of the video with little or no content, the techniques provide a more integrated, resource-efficient, and generally improved ad experience for users. For example, the user experience for video content such as cooking videos, exercise videos, or other videos with natural pauses can be improved by such analysis and ad insertion
SMIL State: an architecture and implementation for adaptive time-based web applications
In this paper we examine adaptive time-based web applications (or presentations). These are interactive presentations where time dictates which parts of the application are presented (providing the major structuring paradigm), and that require interactivity and other dynamic adaptation. We investigate the current technologies available to create such presentations and their shortcomings, and suggest a mechanism for addressing these shortcomings. This mechanism, SMIL State, can be used to add user-defined state to declarative time-based languages such as SMIL or SVG animation, thereby enabling the author to create control flows that are difficult to realize within the temporal containment model of the host languages. In addition, SMIL State can be used as a bridging mechanism between languages, enabling easy integration of external components into the web application. Finally, SMIL State enables richer expressions for content control. This paper defines SMIL State in terms of an introductory example, followed by a detailed specification of the State model. Next, the implementation of this model is discussed. We conclude with a set of potential use cases, including dynamic content adaptation and delayed insertion of custom content such as advertisements. © 2009 Springer Science+Business Media, LLC
Extensible Detection and Indexing of Highlight Events in Broadcasted Sports Video
Content-based indexing is fundamental to support and sustain the ongoing growth of broadcasted sports video. The main challenge is to design extensible frameworks to detect and index highlight events. This paper presents: 1) A statistical-driven event detection approach that utilizes a minimum amount of manual knowledge and is based on a universal scope-of-detection and audio-visual features; 2) A semi-schema-based indexing that combines the benefits of schema-based modeling to ensure that the video indexes are valid at all time without manual checking, and schema-less modeling to allow several passes of instantiation in which additional elements can be declared. To demonstrate the performance of the events detection, a large dataset of sport videos with a total of around 15 hours including soccer, basketball and Australian football is used
Entropy-Based Dynamic Ad Placement Algorithms for In-Video Advertising
With the evolution of the Internet and the increasing number of users over last years, online
advertising has become one of the pillars models that sustains many of the Internet businesses.
In this dissertation, we review the history of online advertising, will be made, as well as the
state-of-the-art of the major scientific contributions in online advertising,in particularly in
respect to in-video advertising.
In in-video advertising, one of the major issues is to identify the best places for insertion of
ads. In the literature, this problem is addressed in different ways. Some methods are designed
for a specific genres of video, e.g., football or tennis, while others are independent of genre,
trying to identify the meaningful video scenes (a set of continuous and related frames) where
ads will be displayed.
However, the vast majority of online videos in the Internet are not long enough to identify
large scenes. So, in this dissertation we will address a new solution for advertisement insertion
in online videos, a solution that can be utilized independently of the duration and genre of the
video in question.
When developing a solution for in-video advertising, a major challenge rests on the intrusiveness
that the ad inserted will take upon the viewer. The intrusiveness is related to the place and
timing used by the advertising to be inserted. For these reasons, the algorithm has to take in
consideration the "where", "when" and "how" the advertisement should be inserted in the video,
so that it is possible to reduce the intrusiveness of the ads to the viewer.
In short, in addition to besides being independent of duration and genre, the proposed method
for ad placement in video was developed taking in consideration the ad intrusiveness to the
user.Com a evolução da Internet e o nĂșmero crescente de utilizadores ao longo destes Ășltimos anos,
a publicidade on-line tornou-se um dos modelos base que tem sustentado muitos negĂłcios na
Internet. Da mesma forma, vĂdeos on-line constituem uma parte significativa do trĂĄfego na
Internet. Ă por isso possĂvel entender desta forma, o potencial que ferramentas que possĂŁo
explorar eficientemente ambas estas ĂĄreas possuem no mercado.
Nesta dissertação serå feita uma revisão da história da publicidade online, mas também serå
apresentado ao leitor uma revisĂŁo sobre o estado da arte das principais contribuiçÔes cientĂficas
para a publicidade on-line, em especial para a publicidade em video.
Na publicidade em vĂdeo, uma das principais preocupaçÔes Ă© identificar os melhores locais para
a inserir os anĂșncios. Na literatura, este problema Ă© abordado de diferentes maneiras, alguns
criaram mĂ©todos para gĂȘneros especĂficos de vĂdeo, por exemplo, futebol ou tĂ©nis, outros
mĂ©todos sĂŁo independentes do gĂȘnero, mas tentam identificar as cenas de vĂdeo (um conjunto
contĂnuo de frames relacionadas) e apenas exibir anĂșncios neles.
No entanto, a grande maioria dos vĂdeos on-line na Internet nĂŁo sĂŁo suficiente longos para serem
identificadas cenas suficientemente longas para inserir os anĂșncios. Assim, nesta dissertação
iremos abordar uma nova solução para a inserção de anĂșnicios em vĂdeos, uma solução que
pode ser utilizada de forma independente da duração e gĂȘnero do vĂdeo em questĂŁo.
Ao desenvolver uma solução para inserir anĂșncos em vĂdeos a grande preocupação recai sobre
a intromissĂŁo que o anĂșncio inserido poderĂĄ ter sobre o utilizador. A intrusĂŁo estĂĄ relacionada
com o local e tempo utilizado pela publicidade quando é inserida. Por estas razÔes, o algoritmo
tem que levar em consideração "onde", "quando" e "como" o anĂșncio deve ser inserido no vĂdeo,
de modo que seja possĂvel reduzir a intromissĂŁo dos anĂșncios para o utilizador.
Em suma, para alĂ©m de ser independente da duração e gĂȘnero do vĂdeo, o mĂ©todo proposto
serĂĄ tambĂ©m desenvolvido tendo em consideração a intromissĂĄo do anĂșncio para o utilizador.
Por fim, o método proposto serå testado e comparado com outros métodos, de modo a que seja
possivel perceber as suas capacidades
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