Circulant temporal encoding for video retrieval and temporal alignment

We address the problem of specific video event retrieval. Given a query video of a specific event, e.g., a concert of Madonna, the goal is to retrieve other videos of the same event that temporally overlap with the query. Our approach encodes the frame descriptors of a video to jointly represent their appearance and temporal order. It exploits the properties of circulant matrices to efficiently compare the videos in the frequency domain. This offers a significant gain in complexity and accurately localizes the matching parts of videos. The descriptors can be compressed in the frequency domain with a product quantizer adapted to complex numbers. In this case, video retrieval is performed without decompressing the descriptors. We also consider the temporal alignment of a set of videos. We exploit the matching confidence and an estimate of the temporal offset computed for all pairs of videos by our retrieval approach. Our robust algorithm aligns the videos on a global timeline by maximizing the set of temporally consistent matches. The global temporal alignment enables synchronous playback of the videos of a given scene

Feature based dynamic intra-video indexing

A thesis submitted in partial fulfillment for the degree of Doctor of PhilosophyWith the advent of digital imagery and its wide spread application in all vistas of life, it has become an important component in the world of communication. Video content ranging from broadcast news, sports, personal videos, surveillance, movies and entertainment and similar domains is increasing exponentially in quantity and it is becoming a challenge to retrieve content of interest from the corpora. This has led to an increased interest amongst the researchers to investigate concepts of video structure analysis, feature extraction, content annotation, tagging, video indexing, querying and retrieval to fulfil the requirements. However, most of the previous work is confined within specific domain and constrained by the quality, processing and storage capabilities. This thesis presents a novel framework agglomerating the established approaches from feature extraction to browsing in one system of content based video retrieval. The proposed framework significantly fills the gap identified while satisfying the imposed constraints of processing, storage, quality and retrieval times. The output entails a framework, methodology and prototype application to allow the user to efficiently and effectively retrieved content of interest such as age, gender and activity by specifying the relevant query. Experiments have shown plausible results with an average precision and recall of 0.91 and 0.92 respectively for face detection using Haar wavelets based approach. Precision of age ranges from 0.82 to 0.91 and recall from 0.78 to 0.84. The recognition of gender gives better precision with males (0.89) compared to females while recall gives a higher value with females (0.92). Activity of the subject has been detected using Hough transform and classified using Hiddell Markov Model. A comprehensive dataset to support similar studies has also been developed as part of the research process. A Graphical User Interface (GUI) providing a friendly and intuitive interface has been integrated into the developed system to facilitate the retrieval process. The comparison results of the intraclass correlation coefficient (ICC) shows that the performance of the system closely resembles with that of the human annotator. The performance has been optimised for time and error rate

A time flexible kernel framework for video-based activity recognition

This work deals with the challenging task of activity recognition in unconstrained videos. Standard methods are based on video encoding of low-level features using Fisher Vectors or Bag of Features. However, these approaches model every sequence into a single vector with fixed dimensionality that lacks any long-term temporal information, which may be important for recognition, especially of complex activities. This work proposes a novel framework with two main technical novelties: First, a video encoding method that maintains the temporal structure of sequences and second a Time Flexible Kernel that allows comparison of sequences of different lengths and random alignment. Results on challenging benchmarks and comparison to previous work demonstrate the applicability and value of our framework

Robust Subspace Estimation Using Low-rank Optimization. Theory And Applications In Scene Reconstruction, Video Denoising, And Activity Recognition.

In this dissertation, we discuss the problem of robust linear subspace estimation using low-rank optimization and propose three formulations of it. We demonstrate how these formulations can be used to solve fundamental computer vision problems, and provide superior performance in terms of accuracy and running time. Consider a set of observations extracted from images (such as pixel gray values, local features, trajectories . . . etc). If the assumption that these observations are drawn from a liner subspace (or can be linearly approximated) is valid, then the goal is to represent each observation as a linear combination of a compact basis, while maintaining a minimal reconstruction error. One of the earliest, yet most popular, approaches to achieve that is Principal Component Analysis (PCA). However, PCA can only handle Gaussian noise, and thus suffers when the observations are contaminated with gross and sparse outliers. To this end, in this dissertation, we focus on estimating the subspace robustly using low-rank optimization, where the sparse outliers are detected and separated through the `1 norm. The robust estimation has a two-fold advantage: First, the obtained basis better represents the actual subspace because it does not include contributions from the outliers. Second, the detected outliers are often of a specific interest in many applications, as we will show throughout this thesis. We demonstrate four different formulations and applications for low-rank optimization. First, we consider the problem of reconstructing an underwater sequence by removing the iii turbulence caused by the water waves. The main drawback of most previous attempts to tackle this problem is that they heavily depend on modelling the waves, which in fact is ill-posed since the actual behavior of the waves along with the imaging process are complicated and include several noise components; therefore, their results are not satisfactory. In contrast, we propose a novel approach which outperforms the state-of-the-art. The intuition behind our method is that in a sequence where the water is static, the frames would be linearly correlated. Therefore, in the presence of water waves, we may consider the frames as noisy observations drawn from a the subspace of linearly correlated frames. However, the noise introduced by the water waves is not sparse, and thus cannot directly be detected using low-rank optimization. Therefore, we propose a data-driven two-stage approach, where the first stage “sparsifies” the noise, and the second stage detects it. The first stage leverages the temporal mean of the sequence to overcome the structured turbulence of the waves through an iterative registration algorithm. The result of the first stage is a high quality mean and a better structured sequence; however, the sequence still contains unstructured sparse noise. Thus, we employ a second stage at which we extract the sparse errors from the sequence through rank minimization. Our method converges faster, and drastically outperforms state of the art on all testing sequences. Secondly, we consider a closely related situation where an independently moving object is also present in the turbulent video. More precisely, we consider video sequences acquired in a desert battlefields, where atmospheric turbulence is typically present, in addition to independently moving targets. Typical approaches for turbulence mitigation follow averaging or de-warping techniques. Although these methods can reduce the turbulence, they distort the independently moving objects which can often be of great interest. Therefore, we address the iv problem of simultaneous turbulence mitigation and moving object detection. We propose a novel three-term low-rank matrix decomposition approach in which we decompose the turbulence sequence into three components: the background, the turbulence, and the object. We simplify this extremely difficult problem into a minimization of nuclear norm, Frobenius norm, and `1 norm. Our method is based on two observations: First, the turbulence causes dense and Gaussian noise, and therefore can be captured by Frobenius norm, while the moving objects are sparse and thus can be captured by `1 norm. Second, since the object’s motion is linear and intrinsically different than the Gaussian-like turbulence, a Gaussian-based turbulence model can be employed to enforce an additional constraint on the search space of the minimization. We demonstrate the robustness of our approach on challenging sequences which are significantly distorted with atmospheric turbulence and include extremely tiny moving objects. In addition to robustly detecting the subspace of the frames of a sequence, we consider using trajectories as observations in the low-rank optimization framework. In particular, in videos acquired by moving cameras, we track all the pixels in the video and use that to estimate the camera motion subspace. This is particularly useful in activity recognition, which typically requires standard preprocessing steps such as motion compensation, moving object detection, and object tracking. The errors from the motion compensation step propagate to the object detection stage, resulting in miss-detections, which further complicates the tracking stage, resulting in cluttered and incorrect tracks. In contrast, we propose a novel approach which does not follow the standard steps, and accordingly avoids the aforementioned diffi- culties. Our approach is based on Lagrangian particle trajectories which are a set of dense trajectories obtained by advecting optical flow over time, thus capturing the ensemble motions v of a scene. This is done in frames of unaligned video, and no object detection is required. In order to handle the moving camera, we decompose the trajectories into their camera-induced and object-induced components. Having obtained the relevant object motion trajectories, we compute a compact set of chaotic invariant features, which captures the characteristics of the trajectories. Consequently, a SVM is employed to learn and recognize the human actions using the computed motion features. We performed intensive experiments on multiple benchmark datasets, and obtained promising results. Finally, we consider a more challenging problem referred to as complex event recognition, where the activities of interest are complex and unconstrained. This problem typically pose significant challenges because it involves videos of highly variable content, noise, length, frame size . . . etc. In this extremely challenging task, high-level features have recently shown a promising direction as in [53, 129], where core low-level events referred to as concepts are annotated and modelled using a portion of the training data, then each event is described using its content of these concepts. However, because of the complex nature of the videos, both the concept models and the corresponding high-level features are significantly noisy. In order to address this problem, we propose a novel low-rank formulation, which combines the precisely annotated videos used to train the concepts, with the rich high-level features. Our approach finds a new representation for each event, which is not only low-rank, but also constrained to adhere to the concept annotation, thus suppressing the noise, and maintaining a consistent occurrence of the concepts in each event. Extensive experiments on large scale real world dataset TRECVID Multimedia Event Detection 2011 and 2012 demonstrate that our approach consistently improves the discriminativity of the high-level features by a significant margin

Automatic mashup generation of multiple-camera videos

The amount of user generated video content is growing enormously with the increase in availability and affordability of technologies for video capturing (e.g. camcorders, mobile-phones), storing (e.g. magnetic and optical devices, online storage services), and sharing (e.g. broadband internet, social networks). It has become a common sight at social occasions like parties, concerts, weddings, vacations that many people are shooting videos at approximately the same time. Such concurrent recordings provide multiple views of the same event. In professional video production, the use of multiple cameras is very common. In order to compose an interesting video to watch, audio and video segments from different recordings are mixed into a single video stream. However, in case of non-professional recordings, mixing different camera recordings is not common as the process is considered very time consuming and requires expertise to do. In this thesis, we research on how to automatically combine multiple-camera recordings in a single video stream, called as a mashup. Since non-professional recordings, in general, are characterized by low signal quality and lack of artistic appeal, our objective is to use mashups to enrich the viewing experience of such recordings. In order to define a target application and collect requirements for a mashup, we conducted a study by involving experts on video editing and general camera users by means of interviews and focus groups. Based on the study results, we decided to work on the domain of concert video. We listed the requirements for concert video mashups such as image quality, diversity, and synchronization. According to the requirements, we proposed a solution approach for mashup generation and introduced a formal model consisting of pre-processing, mashupcomposition and post-processing steps. This thesis describes the pre-processing and mashup-composition steps, which result in the automatic generation of a mashup satisfying a set of the elicited requirements. At the pre-processing step, we synchronized multiple-camera recordings to be represented in a common time-line. We proposed and developed synchronization methods based on detecting and matching audio and video features extracted from the recorded content. We developed three realizations of the approach using different features: still-camera flashes in video, audio-fingerprints and audio-onsets. The realizations are independent of the frame rate of the recordings, the number of cameras and provide the synchronization offset accuracy at frame level. Based on their performance in a common data-set, audio-fingerprint and audio-onset were found as the most suitable to apply in generating mashups of concert videos. In the mashup-composition step, we proposed an optimization based solution to compose a mashup from the synchronized recordings. The solution is based on maximizing an objective function containing a number of parameters, which represent the requirements that influence the mashup quality. The function is subjected to a number of constraints, which represent the requirements that must be fulfilled in a mashup. Different audio-visual feature extraction and analysis techniques were employed to measure the degree of fulfillment of the requirements represented in the objective function. We developed an algorithm, first-fit, to compose a mashup satisfying the constraints and maximizing the objective function. Finally, to validate our solution approach, we evaluated the mashups generated by the first-fit algorithm with the ones generated by two other methods. In the first method, naive, a mashup was generated by satisfying only the requirements given as constraints and in the second method, manual, a mashup was created by a professional. In the objective evaluation, first-fit mashups scored higher than both the manual and naive mashups. To assess the end-user satisfaction, we also conducted a user study where we measured user preferences on the mashups generated by the three methods on different aspects of mashup quality. In all the aspects, the naive mashup scored significantly low, while the manual and first-fit mashups scored similarly. We can conclude that the perceived quality of a mashup generated by the naive method is lower than first-fit and manual while the perceived quality of the mashups generated by first-fit and manual methods are similar

Media aesthetics based multimedia storytelling.

Since the earliest of times, humans have been interested in recording their life experiences, for future reference and for storytelling purposes. This task of recording experiences --i.e., both image and video capture-- has never before in history been as easy as it is today. This is creating a digital information overload that is becoming a great concern for the people that are trying to preserve their life experiences. As high-resolution digital still and video cameras become increasingly pervasive, unprecedented amounts of multimedia, are being downloaded to personal hard drives, and also uploaded to online social networks on a daily basis. The work presented in this dissertation is a contribution in the area of multimedia organization, as well as automatic selection of media for storytelling purposes, which eases the human task of summarizing a collection of images or videos in order to be shared with other people. As opposed to some prior art in this area, we have taken an approach in which neither user generated tags nor comments --that describe the photographs, either in their local or on-line repositories-- are taken into account, and also no user interaction with the algorithms is expected. We take an image analysis approach where both the context images --e.g. images from online social networks to which the image stories are going to be uploaded--, and the collection images --i.e., the collection of images or videos that needs to be summarized into a story--, are analyzed using image processing algorithms. This allows us to extract relevant metadata that can be used in the summarization process. Multimedia-storytellers usually follow three main steps when preparing their stories: first they choose the main story characters, the main events to describe, and finally from these media sub-groups, they choose the media based on their relevance to the story as well as based on their aesthetic value. Therefore, one of the main contributions of our work has been the design of computational models --both regression based, as well as classification based-- that correlate well with human perception of the aesthetic value of images and videos. These computational aesthetics models have been integrated into automatic selection algorithms for multimedia storytelling, which are another important contribution of our work. A human centric approach has been used in all experiments where it was feasible, and also in order to assess the final summarization results, i.e., humans are always the final judges of our algorithms, either by inspecting the aesthetic quality of the media, or by inspecting the final story generated by our algorithms. We are aware that a perfect automatically generated story summary is very hard to obtain, given the many subjective factors that play a role in such a creative process; rather, the presented approach should be seen as a first step in the storytelling creative process which removes some of the ground work that would be tedious and time consuming for the user. Overall, the main contributions of this work can be capitalized in three: (1) new media aesthetics models for both images and videos that correlate with human perception, (2) new scalable multimedia collection structures that ease the process of media summarization, and finally, (3) new media selection algorithms that are optimized for multimedia storytelling purposes.Postprint (published version

Temporal multimodal video and lifelog retrieval

The past decades have seen exponential growth of both consumption and production of data, with multimedia such as images and videos contributing significantly to said growth. The widespread proliferation of smartphones has provided everyday users with the ability to consume and produce such content easily. As the complexity and diversity of multimedia data has grown, so has the need for more complex retrieval models which address the information needs of users. Finding relevant multimedia content is central in many scenarios, from internet search engines and medical retrieval to querying one's personal multimedia archive, also called lifelog. Traditional retrieval models have often focused on queries targeting small units of retrieval, yet users usually remember temporal context and expect results to include this. However, there is little research into enabling these information needs in interactive multimedia retrieval. In this thesis, we aim to close this research gap by making several contributions to multimedia retrieval with a focus on two scenarios, namely video and lifelog retrieval. We provide a retrieval model for complex information needs with temporal components, including a data model for multimedia retrieval, a query model for complex information needs, and a modular and adaptable query execution model which includes novel algorithms for result fusion. The concepts and models are implemented in vitrivr, an open-source multimodal multimedia retrieval system, which covers all aspects from extraction to query formulation and browsing. vitrivr has proven its usefulness in evaluation campaigns and is now used in two large-scale interdisciplinary research projects. We show the feasibility and effectiveness of our contributions in two ways: firstly, through results from user-centric evaluations which pit different user-system combinations against one another. Secondly, we perform a system-centric evaluation by creating a new dataset for temporal information needs in video and lifelog retrieval with which we quantitatively evaluate our models. The results show significant benefits for systems that enable users to specify more complex information needs with temporal components. Participation in interactive retrieval evaluation campaigns over multiple years provides insight into possible future developments and challenges of such campaigns

Deep face tracking and parsing in the wild

Face analysis has been a long-standing research direction in the field of computer vision and pattern recognition. A complete face analysis system involves solving several tasks including face detection, face tracking, face parsing, and face recognition. Recently, the performance of methods in all tasks has significantly improved thanks to the employment of Deep Convolutional Neural Networks (DCNNs). However, existing face analysis algorithms mainly focus on solving facial images captured in the constrained laboratory environment, and their performance on real-world images has remained less explored. Compared with the lab environment, the in-the-wild settings involve greater diversity in face sizes, poses, facial expressions, background clutters, lighting conditions and imaging quality. This thesis investigates two fundamental tasks in face analysis under in-the-wild settings: face tracking and face parsing. Both tasks serve as important prerequisites for downstream face analysis applications. However, in-the-wild datasets remain scarce in both fields and models have not been rigorously evaluated in such settings. In this thesis, we aim to bridge that gap of lacking in-the-wild data, evaluate existing methods in these settings, and develop accurate, robust and efficient deep learning-based methods for the two tasks. For face tracking in the wild, we introduce the first in-the-wild face tracking dataset, MobiFace, that consists of 80 videos captured by mobile phones during mobile live-streaming. The environment of the live-streaming performance is fully unconstrained and the interactions between users and mobile phones are natural and spontaneous. Next, we evaluate existing tracking methods, including generic object trackers and dedicated face trackers. The results show that MobiFace represent unique challenges in face tracking in the wild and cannot be readily solved by existing methods. Finally, we present a DCNN-based framework, FT-RCNN, that significantly outperforms other methods in face tracking in the wild. For face parsing in the wild, we introduce the first large-scale in-the-wild face dataset, iBugMask, that contains 21, 866 training images and 1, 000 testing images. Unlike existing datasets, the images in iBugMask are captured in the fully unconstrained environment and are not cropped or preprocessed of any kind. Manually annotated per-pixel labels for eleven facial regions are provided for each target face. Next, we benchmark existing parsing methods and the results show that iBugMask is extremely challenging for all methods. By rigorous benchmarking, we observe that the pre-processing of facial images with bounding boxes in face parsing in the wild introduces bias. When cropping the face with a bounding box, a cropping margin has to be hand-picked. If face alignment is used, fiducial landmarks are required and a predefined alignment template has to be selected. These additional hyper-parameters have to be carefully considered and can have a significant impact on the face parsing performance. To solve this, we propose Region-of-Interest (RoI) Tanh-polar transform that warps the whole image to a fixed-sized representation. Moreover, the RoI Tanh-polar transform is differentiable and allows for rotation equivariance in 1 DCNNs. We show that when coupled with a simple Fully Convolutional Network, our RoI Tanh-polar transformer Network has achieved state-of-the-art results on face parsing in the wild. This thesis contributes towards in-the-wild face tracking and face parsing by providing novel datasets and proposing effective frameworks. Both tasks can benefit real-world downstream applications such as facial age estimation, facial expression recognition and lip-reading. The proposed RoI Tanh-polar transform also provides a new perspective in how to preprocess the face images and make the DCNNs truly end-to-end for real-world face analysis applications.Open Acces