278 research outputs found

    Video copy detection using multiple visual cues and MPEG-7 descriptors

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    We propose a video copy detection framework that detects copy segments by fusing the results of three different techniques: facial shot matching, activity subsequence matching, and non-facial shot matching using low-level features. In facial shot matching part, a high-level face detector identifies facial frames/shots in a video clip. Matching faces with extended body regions gives the flexibility to discriminate the same person (e.g., an anchor man or a political leader) in different events or scenes. In activity subsequence matching part, a spatio-temporal sequence matching technique is employed to match video clips/segments that are similar in terms of activity. Lastly, the non-facial shots are matched using low-level MPEG-7 descriptors and dynamic-weighted feature similarity calculation. The proposed framework is tested on the query and reference dataset of CBCD task of TRECVID 2008. Our results are compared with the results of top-8 most successful techniques submitted to this task. Promising results are obtained in terms of both effectiveness and efficiency. © 2010 Elsevier Inc. All rights reserved

    Aggregating Local Features into Bundles for High-Precision Object Retrieval

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    Due to the omnipresence of digital cameras and mobile phones the number of images stored in image databases has grown tremendously in the last years. It becomes apparent that new data management and retrieval techniques are needed to deal with increasingly large image databases. This thesis presents new techniques for content-based image retrieval where the image content itself is used to retrieve images by visual similarity from databases. We focus on the query-by-example scenario, assuming the image itself is provided as query to the retrieval engine. In many image databases, images are often associated with metadata, which may be exploited to improve the retrieval performance. In this work, we present a technique that fuses cues from the visual domain and textual annotations into a single compact representation. This combined multimodal representation performs significantly better compared to the underlying unimodal representations, which we demonstrate on two large-scale image databases consisting of up to 10 million images. The main focus of this work is on feature bundling for object retrieval and logo recognition. We present two novel feature bundling techniques that aggregate multiple local features into a single visual description. In contrast to many other works, both approaches encode geometric information about the spatial layout of local features into the corresponding visual description itself. Therefore, these descriptions are highly distinctive and suitable for high-precision object retrieval. We demonstrate the use of both bundling techniques for logo recognition. Here, the recognition is performed by the retrieval of visually similar images from a database of reference images, making the recognition systems easily scalable to a large number of classes. The results show that our retrieval-based methods can successfully identify small objects such as logos with an extremely low false positive rate. In particular, our feature bundling techniques are beneficial because false positives are effectively avoided upfront due to the highly distinctive descriptions. We further demonstrate and thoroughly evaluate the use of our bundling technique based on min-Hashing for image and object retrieval. Compared to approaches based on conventional bag-of-words retrieval, it has much higher efficiency: the retrieved result lists are shorter and cleaner while recall is on equal level. The results suggest that this bundling scheme may act as pre-filtering step in a wide range of scenarios and underline the high effectiveness of this approach. Finally, we present a new variant for extremely fast re-ranking of retrieval results, which ranks the retrieved images according to the spatial consistency of their local features to those of the query image. The demonstrated method is robust to outliers, performs better than existing methods and allows to process several hundreds to thousands of images per second on a single thread

    Deep Architectures for Visual Recognition and Description

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    In recent times, digital media contents are inherently of multimedia type, consisting of the form text, audio, image and video. Several of the outstanding computer Vision (CV) problems are being successfully solved with the help of modern Machine Learning (ML) techniques. Plenty of research work has already been carried out in the field of Automatic Image Annotation (AIA), Image Captioning and Video Tagging. Video Captioning, i.e., automatic description generation from digital video, however, is a different and complex problem altogether. This study compares various existing video captioning approaches available today and attempts their classification and analysis based on different parameters, viz., type of captioning methods (generation/retrieval), type of learning models employed, the desired output description length generated, etc. This dissertation also attempts to critically analyze the existing benchmark datasets used in various video captioning models and the evaluation metrics for assessing the final quality of the resultant video descriptions generated. A detailed study of important existing models, highlighting their comparative advantages as well as disadvantages are also included. In this study a novel approach for video captioning on the Microsoft Video Description (MSVD) dataset and Microsoft Video-to-Text (MSR-VTT) dataset is proposed using supervised learning techniques to train a deep combinational framework, for achieving better quality video captioning via predicting semantic tags. We develop simple shallow CNN (2D and 3D) as feature extractors, Deep Neural Networks (DNNs and Bidirectional LSTMs (BiLSTMs) as tag prediction models and Recurrent Neural Networks (RNNs) (LSTM) model as the language model. The aim of the work was to provide an alternative narrative to generating captions from videos via semantic tag predictions and deploy simpler shallower deep model architectures with lower memory requirements as solution so that it is not very memory extensive and the developed models prove to be stable and viable options when the scale of the data is increased. This study also successfully employed deep architectures like the Convolutional Neural Network (CNN) for speeding up automation process of hand gesture recognition and classification of the sign languages of the Indian classical dance form, ‘Bharatnatyam’. This hand gesture classification is primarily aimed at 1) building a novel dataset of 2D single hand gestures belonging to 27 classes that were collected from (i) Google search engine (Google images), (ii) YouTube videos (dynamic and with background considered) and (iii) professional artists under staged environment constraints (plain backgrounds). 2) exploring the effectiveness of CNNs for identifying and classifying the single hand gestures by optimizing the hyperparameters, and 3) evaluating the impacts of transfer learning and double transfer learning, which is a novel concept explored for achieving higher classification accuracy

    A picture is worth a thousand words : content-based image retrieval techniques

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    In my dissertation I investigate techniques for improving the state of the art in content-based image retrieval. To place my work into context, I highlight the current trends and challenges in my field by analyzing over 200 recent articles. Next, I propose a novel paradigm called __artificial imagination__, which gives the retrieval system the power to imagine and think along with the user in terms of what she is looking for. I then introduce a new user interface for visualizing and exploring image collections, empowering the user to navigate large collections based on her own needs and preferences, while simultaneously providing her with an accurate sense of what the database has to offer. In the later chapters I present work dealing with millions of images and focus in particular on high-performance techniques that minimize memory and computational use for both near-duplicate image detection and web search. Finally, I show early work on a scene completion-based image retrieval engine, which synthesizes realistic imagery that matches what the user has in mind.LEI Universiteit LeidenNWOImagin

    Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints

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    The continuous stream of videos that are uploaded and shared on the Internet has been leveraged by computer vision researchers for a myriad of detection and retrieval tasks, including gesture detection, copy detection, face authentication, etc. However, the existing state-of-the-art event detection and retrieval techniques fail to deal with several real-world challenges (e.g., low resolution, low brightness and noise) under adversary constraints. This dissertation focuses on these challenges in realistic scenarios and demonstrates practical methods to address the problem of robustness and efficiency within video event detection and retrieval systems in five application settings (namely, CAPTCHA decoding, face liveness detection, reconstructing typed input on mobile devices, video confirmation attack, and content-based copy detection). Specifically, for CAPTCHA decoding, I propose an automated approach which can decode moving-image object recognition (MIOR) CAPTCHAs faster than humans. I showed that not only are there inherent weaknesses in current MIOR CAPTCHA designs, but that several obvious countermeasures (e.g., extending the length of the codeword) are not viable. More importantly, my work highlights the fact that the choice of underlying hard problem selected by the designers of a leading commercial solution falls into a solvable subclass of computer vision problems. For face liveness detection, I introduce a novel approach to bypass modern face authentication systems. More specifically, by leveraging a handful of pictures of the target user taken from social media, I show how to create realistic, textured, 3D facial models that undermine the security of widely used face authentication solutions. My framework makes use of virtual reality (VR) systems, incorporating along the way the ability to perform animations (e.g., raising an eyebrow or smiling) of the facial model, in order to trick liveness detectors into believing that the 3D model is a real human face. I demonstrate that such VR-based spoofing attacks constitute a fundamentally new class of attacks that point to a serious weaknesses in camera-based authentication systems. For reconstructing typed input on mobile devices, I proposed a method that successfully transcribes the text typed on a keyboard by exploiting video of the user typing, even from significant distances and from repeated reflections. This feat allows us to reconstruct typed input from the image of a mobile phone’s screen on a user’s eyeball as reflected through a nearby mirror, extending the privacy threat to include situations where the adversary is located around a corner from the user. To assess the viability of a video confirmation attack, I explored a technique that exploits the emanations of changes in light to reveal the programs being watched. I leverage the key insight that the observable emanations of a display (e.g., a TV or monitor) during presentation of the viewing content induces a distinctive flicker pattern that can be exploited by an adversary. My proposed approach works successfully in a number of practical scenarios, including (but not limited to) observations of light effusions through the windows, on the back wall, or off the victim’s face. My empirical results show that I can successfully confirm hypotheses while capturing short recordings (typically less than 4 minutes long) of the changes in brightness from the victim’s display from a distance of 70 meters. Lastly, for content-based copy detection, I take advantage of a new temporal feature to index a reference library in a manner that is robust to the popular spatial and temporal transformations in pirated videos. My technique narrows the detection gap in the important area of temporal transformations applied by would-be pirates. My large-scale evaluation on real-world data shows that I can successfully detect infringing content from movies and sports clips with 90.0% precision at a 71.1% recall rate, and can achieve that accuracy at an average time expense of merely 5.3 seconds, outperforming the state of the art by an order of magnitude.Doctor of Philosoph

    Efficient Fully Convolutional Networks for Dense Prediction Tasks

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    Dense prediction is a family of fundamental problems in computer vision, which learns a mapping from input images to complex output structures, including semantic segmentation, depth estimation, and object detection, among many others. Pixel-level labeling is required in such tasks. Deep neural networks have been the dominant solution since the invention of fully-convolutional neural networks (FCNs). Well-designed complicated network structures achieve state-of-the-art performance on benchmark datasets, but often with a high computational cost. The cost will be more expensive when extending to the video sequence. It is important to design efficient fully convolutional networks for dense prediction tasks so that the models can be used on mobile devices in many real-world applications. Light-weight models have drawn much attention recently. Most compact models try to obtain higher accuracy with lower computational cost, but usually, they need to make the trade-off between accuracy and efficiency. Besides, it is hard to train a compact model properly with limited model capacity. Thus, we target improving the performance of fully convolutional networks by using extra constraints during the training process to keep the efficiency of the inference. Our study starts with knowledge distillation, which has been verified valid in classification tasks. The compact models are trained with the help of large models. We design several new distillation methods for capturing the structure information, taking into account the fact that dense prediction is a structured prediction problem. Moreover, we extend the distillation methods to the video sequence and design temporal knowledge distillation. Both the temporal consistency and the accuracy of the compact models can be improved. Except for knowledge distillation, we employ auxiliary modules to provide extra gradients or supervisions in training compact models. Through our training methods, we can improve the performance of compact models without any extra computational costs during inference. The proposed training methods are general and can be applied to various network structures, datasets, and tasks. We mainly conduct our experiments on typical dense prediction tasks, e.g., semantic segmentation with both images and video sequences. We also extend our methods to object detection, depth estimation, and the multi-task learning system. We outperform previous works with a better trade-off between accuracy and efficiency for various dense prediction tasks.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

    Text Similarity Between Concepts Extracted from Source Code and Documentation

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    Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

    Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

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    This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p
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