Najib BEN AOUN | Ecole Nationale d'Ingénieurs de Sfax (ENIS) (original) (raw)

Books by Najib BEN AOUN

Video coding has received an increased interest because of the big growth in the quantity of the ... more Video coding has received an increased interest because of the big growth in the quantity of
the video data. That is why a big interest has been made for developing an efficient video
coding system and improving the motion estimation part which represents the most
important part since it consumes most computation time and most resources used for video
coding. Making the motion estimation a fast and efficient process was the goal of many
researchers. But, unfortunately, that was not reached in the spatial domain. That’s why, new
ME systems have been conducted in other domain such as the frequency and the
multiresolution domain. That is why many studies have been made to improve and simplify
the ME methods. In this chapter, we have studied the wavelet as a domain for ME and we
have proposed a multiresolution motion estimation and compensation method based on
block matching applying in the wavelet coefficients. Because of some problems presented in
this chapter, we have integrated some improvements techniques to ameliorate our ME
system. As a future works, we will reinforce our method with others techniques such as the
spatial segmentation which makes the estimation more accurate by trying to identify real
objects in the predicted moving zones.

Papers by Najib BEN AOUN

Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology, 2012

The bag of words approach describes an image as a histogram of visual words. Therefore, the struc... more The bag of words approach describes an image as a histogram of visual words. Therefore, the structural relation between words is lost. Since graphs are well adapted to represent these structural relations, we propose, in this paper, an image classification framework which draws benefit from the efficiency of the graph in modeling structural information and the good classification performances given by the bag of words method. For each image in the dataset, a graph is created by modeling the spatial relations between dense local patches. Thus, we obtain a graph dataset. From the graph dataset, we select the most frequent subgraphs to construct the bag of subgraphs (BoSG) and we associate to each image a subgraph histogram that describes its visual content. For experiments, we have used the two challenging datasets: 15 Scenes and Pascal VOC 2007. Experimental results show that the proposed method outperforms the bag of words and the spatial pyramid models in terms of recognition rate.

Lecture Notes in Computer Science, 2010

In this paper, we present an overview of a hybrid approach for event detection from video surveil... more In this paper, we present an overview of a hybrid approach for event detection from video surveillance sequences that has been developed within the REGIMVid project. This system can be used to index and search the video sequence by the visual content. The platform provides moving object segmentation and tracking, High-level feature extraction and video event detection. We describe the architecture of the system as well as providing an overview of the descriptors supported to date. We then demonstrate the usefulness of the toolbox in ...

Abstract Recently, the quantity of data has known a big evolution especially with the emergence o... more Abstract Recently, the quantity of data has known a big evolution especially with the emergence of many video applications over networks such as the videophone and the videoconferencing, and multimedia devices such as the high-definition TV and the personal digital assistants. So, it was crucial to reduce the quantity of data stored or transmitted by compressing it spatially and temporally. Hence, motion estimation and compensation are employed in video coding systems to remove temporal redundancy while keeping a high ...

Recognizing video events has been a very active field of interest. The diversity of videos captur... more Recognizing video events has been a very active field of interest. The diversity of videos captured in complex environments and under difficult conditions makes the event recognition a challenging task. In this paper, we present a video event recognition method which exploits the power of graphs for representing the structural organization of the features and the success of the Bag-of-Words approach. Our method combines the Scale Invariant Feature Transform and the Space-Time Interest Point features to characterize the video. To model the spatio-temporal relations among these features, a graph-based representation is used for each video. Then, the video is indexed based on a histogram of frequent sub-graphs. To evaluate our method, we have used the Columbia Consumer Video dataset. The experimental results show the efficiency of the proposed method.

Journal of Visual Communication and Image Representation (JVCIR), Feb 3, 2014

Due to the exponential growth of the video data stored and uploaded in the Internet websites espe... more Due to the exponential growth of the video data stored and uploaded in the Internet websites especially YouTube, an effective analysis of video actions has become very necessary. In this paper, we tackle the challenging problem of human action recognition in realistic video sequences. The proposed system combines the efficiency of the Bag-of-visual-Words strategy and the power of graphs for structural representation of features. It is built upon the commonly used Space–Time Interest Points (STIP) local features followed by a graph-based video representation which models the spatio-temporal relations among these features. The experiments are realized on two challenging datasets: Hollywood2 and UCF YouTube Action. The experimental results show the effectiveness of the proposed method.

International Conference on Signal Processing (ICSP), Jan 1, 2010

"Recently, the quantity of data has known a big evolution especially with the emergence of many ... more "Recently, the quantity of data has known a big
evolution especially with the emergence of many video
applications over networks such as the videophone and the
videoconferencing, and multimedia devices such as the highdefinition
TV and the personal digital assistants. So, it was
crucial to reduce the quantity of data stored or transmitted by
compressing it spatially and temporally. Hence, motion
estimation and compensation are employed in video coding
systems to remove temporal redundancy while keeping a high
visual quality. They are the most important parts of the video
coding process since they require the most computational power
and the biggest consumption in resources and bandwidth.
Therefore, many techniques have been developed to estimate
motion between successive frames. In this paper, we will present
our motion estimation and compensation method applied on the
discrete wavelet transform coefficients and based on the block
matching algorithm which is the simplest, the most efficient and
the most popular technique. Additional techniques are
introduced to accelerate the estimation process and improve the
prediction quality."

Computer Analysis of …, Jan 1, 2011

With the rapid growth of video multimedia databases and the lack of textual descriptions for many... more With the rapid growth of video multimedia databases and the lack of
textual descriptions for many of them, video annotation became a highly
desired task. Conventional systems try to annotate a video query by simply
finding its most similar videos in the database. Although the video annotation
problem has been tackled in the last decade, no attention has been paid to the
problem of assembling video keyframes in a sensed way to provide an answer
of the given video query when no single candidate video turns out to be similar
to the query. In this paper, we introduce a graph based image modeling and
indexing system for video annotation. Our system is able to improve the video
annotation task by assembling a set of graphs representing different keyframes
of different videos, to compose the video query. The experimental results
demonstrate the effectiveness of our system to annotate videos that are not
possibly annotated by classical approaches.

Innovations in Information …, Jan 1, 2011

Video processing and analysis have been an interesting field in research and industry. Informatio... more Video processing and analysis have been an interesting
field in research and industry. Information detection or retrieval
were a challenged task especially with the spread of multimedia
applications and the increased number of the video acquisition
devices such as the surveillance cameras, phones cameras. These
have produced a large amount of video data which are also
diversified and complex. This is what makes event detection in
video a difficult task. Many video event detection methods were
developed which are composed of two fundamental parts: video
indexing and video classification. In this paper, we will introduce
a new video event detection system based on graphs. Our system
models the video frame as a graph in addition to a motion
description. Thereafter, these models were classified and events
are detected. Experimental results proved the effectiveness and
the robustness of our system.

Advanced Concepts for …, Jan 1, 2010

In this paper, we present an overview of a hybrid approach for event detection from video surveil... more In this paper, we present an overview of a hybrid approach
for event detection from video surveillance sequences that has been developed within the REGIMVid project. This system can be used to index and search the video sequence by the visual content. The platform provides moving object segmentation and tracking, High-level feature extraction and video event detection.We describe the architecture of the system as well as providing an overview of the descriptors supported to date. We then demonstrate the usefulness of the toolbox in the context
of feature extraction, events learning and detection in large collection of video surveillance dataset.

Video coding has received an increased interest because of the big growth in the quantity of the ... more Video coding has received an increased interest because of the big growth in the quantity of
the video data. That is why a big interest has been made for developing an efficient video
coding system and improving the motion estimation part which represents the most
important part since it consumes most computation time and most resources used for video
coding. Making the motion estimation a fast and efficient process was the goal of many
researchers. But, unfortunately, that was not reached in the spatial domain. That’s why, new
ME systems have been conducted in other domain such as the frequency and the
multiresolution domain. That is why many studies have been made to improve and simplify
the ME methods. In this chapter, we have studied the wavelet as a domain for ME and we
have proposed a multiresolution motion estimation and compensation method based on
block matching applying in the wavelet coefficients. Because of some problems presented in
this chapter, we have integrated some improvements techniques to ameliorate our ME
system. As a future works, we will reinforce our method with others techniques such as the
spatial segmentation which makes the estimation more accurate by trying to identify real
objects in the predicted moving zones.

Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology, 2012

The bag of words approach describes an image as a histogram of visual words. Therefore, the struc... more The bag of words approach describes an image as a histogram of visual words. Therefore, the structural relation between words is lost. Since graphs are well adapted to represent these structural relations, we propose, in this paper, an image classification framework which draws benefit from the efficiency of the graph in modeling structural information and the good classification performances given by the bag of words method. For each image in the dataset, a graph is created by modeling the spatial relations between dense local patches. Thus, we obtain a graph dataset. From the graph dataset, we select the most frequent subgraphs to construct the bag of subgraphs (BoSG) and we associate to each image a subgraph histogram that describes its visual content. For experiments, we have used the two challenging datasets: 15 Scenes and Pascal VOC 2007. Experimental results show that the proposed method outperforms the bag of words and the spatial pyramid models in terms of recognition rate.

Lecture Notes in Computer Science, 2010

In this paper, we present an overview of a hybrid approach for event detection from video surveil... more In this paper, we present an overview of a hybrid approach for event detection from video surveillance sequences that has been developed within the REGIMVid project. This system can be used to index and search the video sequence by the visual content. The platform provides moving object segmentation and tracking, High-level feature extraction and video event detection. We describe the architecture of the system as well as providing an overview of the descriptors supported to date. We then demonstrate the usefulness of the toolbox in ...

Abstract Recently, the quantity of data has known a big evolution especially with the emergence o... more Abstract Recently, the quantity of data has known a big evolution especially with the emergence of many video applications over networks such as the videophone and the videoconferencing, and multimedia devices such as the high-definition TV and the personal digital assistants. So, it was crucial to reduce the quantity of data stored or transmitted by compressing it spatially and temporally. Hence, motion estimation and compensation are employed in video coding systems to remove temporal redundancy while keeping a high ...

Recognizing video events has been a very active field of interest. The diversity of videos captur... more Recognizing video events has been a very active field of interest. The diversity of videos captured in complex environments and under difficult conditions makes the event recognition a challenging task. In this paper, we present a video event recognition method which exploits the power of graphs for representing the structural organization of the features and the success of the Bag-of-Words approach. Our method combines the Scale Invariant Feature Transform and the Space-Time Interest Point features to characterize the video. To model the spatio-temporal relations among these features, a graph-based representation is used for each video. Then, the video is indexed based on a histogram of frequent sub-graphs. To evaluate our method, we have used the Columbia Consumer Video dataset. The experimental results show the efficiency of the proposed method.

Journal of Visual Communication and Image Representation (JVCIR), Feb 3, 2014

Due to the exponential growth of the video data stored and uploaded in the Internet websites espe... more Due to the exponential growth of the video data stored and uploaded in the Internet websites especially YouTube, an effective analysis of video actions has become very necessary. In this paper, we tackle the challenging problem of human action recognition in realistic video sequences. The proposed system combines the efficiency of the Bag-of-visual-Words strategy and the power of graphs for structural representation of features. It is built upon the commonly used Space–Time Interest Points (STIP) local features followed by a graph-based video representation which models the spatio-temporal relations among these features. The experiments are realized on two challenging datasets: Hollywood2 and UCF YouTube Action. The experimental results show the effectiveness of the proposed method.

International Conference on Signal Processing (ICSP), Jan 1, 2010

"Recently, the quantity of data has known a big evolution especially with the emergence of many ... more "Recently, the quantity of data has known a big
evolution especially with the emergence of many video
applications over networks such as the videophone and the
videoconferencing, and multimedia devices such as the highdefinition
TV and the personal digital assistants. So, it was
crucial to reduce the quantity of data stored or transmitted by
compressing it spatially and temporally. Hence, motion
estimation and compensation are employed in video coding
systems to remove temporal redundancy while keeping a high
visual quality. They are the most important parts of the video
coding process since they require the most computational power
and the biggest consumption in resources and bandwidth.
Therefore, many techniques have been developed to estimate
motion between successive frames. In this paper, we will present
our motion estimation and compensation method applied on the
discrete wavelet transform coefficients and based on the block
matching algorithm which is the simplest, the most efficient and
the most popular technique. Additional techniques are
introduced to accelerate the estimation process and improve the
prediction quality."

Computer Analysis of …, Jan 1, 2011

With the rapid growth of video multimedia databases and the lack of textual descriptions for many... more With the rapid growth of video multimedia databases and the lack of
textual descriptions for many of them, video annotation became a highly
desired task. Conventional systems try to annotate a video query by simply
finding its most similar videos in the database. Although the video annotation
problem has been tackled in the last decade, no attention has been paid to the
problem of assembling video keyframes in a sensed way to provide an answer
of the given video query when no single candidate video turns out to be similar
to the query. In this paper, we introduce a graph based image modeling and
indexing system for video annotation. Our system is able to improve the video
annotation task by assembling a set of graphs representing different keyframes
of different videos, to compose the video query. The experimental results
demonstrate the effectiveness of our system to annotate videos that are not
possibly annotated by classical approaches.

Innovations in Information …, Jan 1, 2011

Video processing and analysis have been an interesting field in research and industry. Informatio... more Video processing and analysis have been an interesting
field in research and industry. Information detection or retrieval
were a challenged task especially with the spread of multimedia
applications and the increased number of the video acquisition
devices such as the surveillance cameras, phones cameras. These
have produced a large amount of video data which are also
diversified and complex. This is what makes event detection in
video a difficult task. Many video event detection methods were
developed which are composed of two fundamental parts: video
indexing and video classification. In this paper, we will introduce
a new video event detection system based on graphs. Our system
models the video frame as a graph in addition to a motion
description. Thereafter, these models were classified and events
are detected. Experimental results proved the effectiveness and
the robustness of our system.

Advanced Concepts for …, Jan 1, 2010

In this paper, we present an overview of a hybrid approach for event detection from video surveil... more In this paper, we present an overview of a hybrid approach
for event detection from video surveillance sequences that has been developed within the REGIMVid project. This system can be used to index and search the video sequence by the visual content. The platform provides moving object segmentation and tracking, High-level feature extraction and video event detection.We describe the architecture of the system as well as providing an overview of the descriptors supported to date. We then demonstrate the usefulness of the toolbox in the context
of feature extraction, events learning and detection in large collection of video surveillance dataset.