3,588 research outputs found
Anomaly Detection, Rule Adaptation and Rule Induction Methodologies in the Context of Automated Sports Video Annotation.
Automated video annotation is a topic of considerable interest in computer vision due to its applications in video search, object based video encoding and enhanced broadcast content. The domain of sport broadcasting is, in particular, the subject of current research attention due to its fixed, rule governed, content. This research work aims to develop, analyze and demonstrate novel methodologies that can be useful in the context of adaptive and automated video annotation systems. In this thesis, we present methodologies for addressing the problems of anomaly detection, rule adaptation and rule induction for court based sports such as tennis and badminton. We first introduce an HMM induction strategy for a court-model based method that uses the court structure in the form of a lattice for two related modalities of singles and doubles tennis to tackle the problems of anomaly detection and rectification. We also introduce another anomaly detection methodology that is based on the disparity between the low-level vision based classifiers and the high-level contextual classifier. Another approach to address the problem of rule adaptation is also proposed that employs Convex hulling of the anomalous states. We also investigate a number of novel hierarchical HMM generating methods for stochastic induction of game rules. These methodologies include, Cartesian product Label-based Hierarchical Bottom-up Clustering (CLHBC) that employs prior information within the label structures. A new constrained variant of the classical Chinese Restaurant Process (CRP) is also introduced that is relevant to sports games. We also propose two hybrid methodologies in this context and a comparative analysis is made against the flat Markov model. We also show that these methods are also generalizable to other rule based environments
Spartan Daily, October 3, 2006
Volume 127, Issue 21https://scholarworks.sjsu.edu/spartandaily/10279/thumbnail.jp
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
Spartan Daily, April 8, 2003
Volume 120, Issue 45https://scholarworks.sjsu.edu/spartandaily/9841/thumbnail.jp
A COMPUTATION METHOD/FRAMEWORK FOR HIGH LEVEL VIDEO CONTENT ANALYSIS AND SEGMENTATION USING AFFECTIVE LEVEL INFORMATION
VIDEO segmentation facilitates e±cient video indexing and navigation in large
digital video archives. It is an important process in a content-based video
indexing and retrieval (CBVIR) system. Many automated solutions performed seg-
mentation by utilizing information about the \facts" of the video. These \facts"
come in the form of labels that describe the objects which are captured by the cam-
era. This type of solutions was able to achieve good and consistent results for some
video genres such as news programs and informational presentations. The content
format of this type of videos is generally quite standard, and automated solutions
were designed to follow these format rules. For example in [1], the presence of news
anchor persons was used as a cue to determine the start and end of a meaningful
news segment.
The same cannot be said for video genres such as movies and feature films.
This is because makers of this type of videos utilized different filming techniques to
design their videos in order to elicit certain affective response from their targeted
audience. Humans usually perform manual video segmentation by trying to relate
changes in time and locale to discontinuities in meaning [2]. As a result, viewers
usually have doubts about the boundary locations of a meaningful video segment
due to their different affective responses.
This thesis presents an entirely new view to the problem of high level video
segmentation. We developed a novel probabilistic method for affective level video
content analysis and segmentation. Our method had two stages. In the first stage,
aÂźective content labels were assigned to video shots by means of a dynamic bayesian
0. Abstract 3
network (DBN). A novel hierarchical-coupled dynamic bayesian network (HCDBN)
topology was proposed for this stage. The topology was based on the pleasure-
arousal-dominance (P-A-D) model of aÂźect representation [3]. In principle, this
model can represent a large number of emotions. In the second stage, the visual,
audio and aÂźective information of the video was used to compute a statistical feature
vector to represent the content of each shot. Affective level video segmentation was
achieved by applying spectral clustering to the feature vectors.
We evaluated the first stage of our proposal by comparing its emotion detec-
tion ability with all the existing works which are related to the field of aÂźective video
content analysis. To evaluate the second stage, we used the time adaptive clustering
(TAC) algorithm as our performance benchmark. The TAC algorithm was the best
high level video segmentation method [2]. However, it is a very computationally
intensive algorithm. To accelerate its computation speed, we developed a modified
TAC (modTAC) algorithm which was designed to be mapped easily onto a field
programmable gate array (FPGA) device. Both the TAC and modTAC algorithms
were used as performance benchmarks for our proposed method.
Since affective video content is a perceptual concept, the segmentation per-
formance and human agreement rates were used as our evaluation criteria. To obtain
our ground truth data and viewer agreement rates, a pilot panel study which was
based on the work of Gross et al. [4] was conducted. Experiment results will show
the feasibility of our proposed method. For the first stage of our proposal, our
experiment results will show that an average improvement of as high as 38% was
achieved over previous works. As for the second stage, an improvement of as high
as 37% was achieved over the TAC algorithm
Spartan Daily, February 24, 2005
Volume 124, Issue 20https://scholarworks.sjsu.edu/spartandaily/10092/thumbnail.jp
- âŠ