11 research outputs found

    Quantifying Object Similarity: Applying Locality Sensitive Hashing for Comparing Material Culture

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    We present a novel technique that compares and quantifies images used here to compare similarities between material cultures. This method is based on locality sensitive hashing (LSH), which uses a relatively fast and flexible algorithm to compare image data and determine their level of similarity. This technique is applied to a dataset of sculpture faces from the Aegean, Anatolia, Cyprus, Egypt, Iran, Indus/Gandhara, the Levant, and Mesopotamia. Results indicate that the objects can be differentiated based on regional differences and show similarities to other locations that share specific material culture traits. Images from known locations enable a network of compared objects to be constructed, where inverse closeness centrality and link weights are used to indicate areas that have a greater or less cultural similarity to other regions. Different periods are assessed, and the results demonstrate that objects from earlier than the 9th century BCE show greater similarity to other local and Egyptian items. Objects from between the 9th and 4th centuries BCE increasingly show inter-regional similarity,with the eastern Mediterranean, including the Aegean, Anatolia, Egypt, and Cyprus,having close similarity to multiple regions. After the 4 th century BCE, greater sculptural similarity is found across a wide area, including the Aegean, Cyprus, Egypt,Mesopotamia, and Gandhara. In general, sculptures from more distant areas increase in similarity in later periods, that is starting from the 9th century BCE. The results demonstrate that the technique can be applied to quantifying object similarity and extended to a broad range of archaeological objects, while also being a tool for rapid analysis that requires minimal data compared to some machine learning techniques.The code and data are provided as part of the outputs

    Visual Concept Detection in Images and Videos

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    The rapidly increasing proliferation of digital images and videos leads to a situation where content-based search in multimedia databases becomes more and more important. A prerequisite for effective image and video search is to analyze and index media content automatically. Current approaches in the field of image and video retrieval focus on semantic concepts serving as an intermediate description to bridge the “semantic gap” between the data representation and the human interpretation. Due to the large complexity and variability in the appearance of visual concepts, the detection of arbitrary concepts represents a very challenging task. In this thesis, the following aspects of visual concept detection systems are addressed: First, enhanced local descriptors for mid-level feature coding are presented. Based on the observation that scale-invariant feature transform (SIFT) descriptors with different spatial extents yield large performance differences, a novel concept detection system is proposed that combines feature representations for different spatial extents using multiple kernel learning (MKL). A multi-modal video concept detection system is presented that relies on Bag-of-Words representations for visual and in particular for audio features. Furthermore, a method for the SIFT-based integration of color information, called color moment SIFT, is introduced. Comparative experimental results demonstrate the superior performance of the proposed systems on the Mediamill and on the VOC Challenge. Second, an approach is presented that systematically utilizes results of object detectors. Novel object-based features are generated based on object detection results using different pooling strategies. For videos, detection results are assembled to object sequences and a shot-based confidence score as well as further features, such as position, frame coverage or movement, are computed for each object class. These features are used as additional input for the support vector machine (SVM)-based concept classifiers. Thus, other related concepts can also profit from object-based features. Extensive experiments on the Mediamill, VOC and TRECVid Challenge show significant improvements in terms of retrieval performance not only for the object classes, but also in particular for a large number of indirectly related concepts. Moreover, it has been demonstrated that a few object-based features are beneficial for a large number of concept classes. On the VOC Challenge, the additional use of object-based features led to a superior performance for the image classification task of 63.8% mean average precision (AP). Furthermore, the generalization capabilities of concept models are investigated. It is shown that different source and target domains lead to a severe loss in concept detection performance. In these cross-domain settings, object-based features achieve a significant performance improvement. Since it is inefficient to run a large number of single-class object detectors, it is additionally demonstrated how a concurrent multi-class object detection system can be constructed to speed up the detection of many object classes in images. Third, a novel, purely web-supervised learning approach for modeling heterogeneous concept classes in images is proposed. Tags and annotations of multimedia data in the WWW are rich sources of information that can be employed for learning visual concepts. The presented approach is aimed at continuous long-term learning of appearance models and improving these models periodically. For this purpose, several components have been developed: a crawling component, a multi-modal clustering component for spam detection and subclass identification, a novel learning component, called “random savanna”, a validation component, an updating component, and a scalability manager. Only a single word describing the visual concept is required to initiate the learning process. Experimental results demonstrate the capabilities of the individual components. Finally, a generic concept detection system is applied to support interdisciplinary research efforts in the field of psychology and media science. The psychological research question addressed in the field of behavioral sciences is, whether and how playing violent content in computer games may induce aggression. Therefore, novel semantic concepts most notably “violence” are detected in computer game videos to gain insights into the interrelationship of violent game events and the brain activity of a player. Experimental results demonstrate the excellent performance of the proposed automatic concept detection approach for such interdisciplinary research

    Enriching unstructured media content about events to enable semi-automated summaries, compilations, and improved search by leveraging social networks

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    (i) Mobile devices and social networks are omnipresent Mobile devices such as smartphones, tablets, or digital cameras together with social networks enable people to create, share, and consume enormous amounts of media items like videos or photos both on the road or at home. Such mobile devices "by pure definition" accompany their owners almost wherever they may go. In consequence, mobile devices are omnipresent at all sorts of events to capture noteworthy moments. Exemplary events can be keynote speeches at conferences, music concerts in stadiums, or even natural catastrophes like earthquakes that affect whole areas or countries. At such events" given a stable network connection" part of the event-related media items are published on social networks both as the event happens or afterwards, once a stable network connection has been established again. (ii) Finding representative media items for an event is hard Common media item search operations, for example, searching for the official video clip for a certain hit record on an online video platform can in the simplest case be achieved based on potentially shallow human-generated metadata or based on more profound content analysis techniques like optical character recognition, automatic speech recognition, or acoustic fingerprinting. More advanced scenarios, however, like retrieving all (or just the most representative) media items that were created at a given event with the objective of creating event summaries or media item compilations covering the event in question are hard, if not impossible, to fulfill at large scale. The main research question of this thesis can be formulated as follows. (iii) Research question "Can user-customizable media galleries that summarize given events be created solely based on textual and multimedia data from social networks?" (iv) Contributions In the context of this thesis, we have developed and evaluated a novel interactive application and related methods for media item enrichment, leveraging social networks, utilizing the Web of Data, techniques known from Content-based Image Retrieval (CBIR) and Content-based Video Retrieval (CBVR), and fine-grained media item addressing schemes like Media Fragments URIs to provide a scalable and near realtime solution to realize the abovementioned scenario of event summarization and media item compilation. (v) Methodology For any event with given event title(s), (potentially vague) event location(s), and (arbitrarily fine-grained) event date(s), our approach can be divided in the following six steps. 1) Via the textual search APIs (Application Programming Interfaces) of different social networks, we retrieve a list of potentially event-relevant microposts that either contain media items directly, or that provide links to media items on external media item hosting platforms. 2) Using third-party Natural Language Processing (NLP) tools, we recognize and disambiguate named entities in microposts to predetermine their relevance. 3) We extract the binary media item data from social networks or media item hosting platforms and relate it to the originating microposts. 4) Using CBIR and CBVR techniques, we first deduplicate exact-duplicate and near-duplicate media items and then cluster similar media items. 5) We rank the deduplicated and clustered list of media items and their related microposts according to well-defined ranking criteria. 6) In order to generate interactive and user-customizable media galleries that visually and audially summarize the event in question, we compile the top-n ranked media items and microposts in aesthetically pleasing and functional ways

    An Energy-Efficient and Reliable Data Transmission Scheme for Transmitter-based Energy Harvesting Networks

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    Energy harvesting technology has been studied to overcome a limited power resource problem for a sensor network. This paper proposes a new data transmission period control and reliable data transmission algorithm for energy harvesting based sensor networks. Although previous studies proposed a communication protocol for energy harvesting based sensor networks, it still needs additional discussion. Proposed algorithm control a data transmission period and the number of data transmission dynamically based on environment information. Through this, energy consumption is reduced and transmission reliability is improved. The simulation result shows that the proposed algorithm is more efficient when compared with previous energy harvesting based communication standard, Enocean in terms of transmission success rate and residual energy.This research was supported by Basic Science Research Program through the National Research Foundation by Korea (NRF) funded by the Ministry of Education, Science and Technology(2012R1A1A3012227)

    IRIM at TRECVID 2012: Semantic Indexing and Instance Search

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    International audienceThe IRIM group is a consortium of French teams work- ing on Multimedia Indexing and Retrieval. This paper describes its participation to the TRECVID 2012 se- mantic indexing and instance search tasks. For the semantic indexing task, our approach uses a six-stages processing pipelines for computing scores for the likeli- hood of a video shot to contain a target concept. These scores are then used for producing a ranked list of im- ages or shots that are the most likely to contain the tar- get concept. The pipeline is composed of the following steps: descriptor extraction, descriptor optimization, classi cation, fusion of descriptor variants, higher-level fusion, and re-ranking. We evaluated a number of dif- ferent descriptors and tried di erent fusion strategies. The best IRIM run has a Mean Inferred Average Pre- cision of 0.2378, which ranked us 4th out of 16 partici- pants. For the instance search task, our approach uses two steps. First individual methods of participants are used to compute similrity between an example image of in- stance and keyframes of a video clip. Then a two-step fusion method is used to combine these individual re- sults and obtain a score for the likelihood of an instance to appear in a video clip. These scores are used to ob- tain a ranked list of clips the most likely to contain the queried instance. The best IRIM run has a MAP of 0.1192, which ranked us 29th on 79 fully automatic runs

    MULTIMEDIA QUESTION ANSWERING AND CONTENT-BASED PRODUCT ANNOTATION AND SEARCH

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    Ph.DDOCTOR OF PHILOSOPH

    Advanced transport operating system software upgrade: Flight management/flight controls software description

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    The Flight Management/Flight Controls (FM/FC) software for the Norden 2 (PDP-11/70M) computer installed on the NASA 737 aircraft is described. The software computes the navigation position estimates, guidance commands, those commands to be issued to the control surfaces to direct the aircraft in flight based on the modes selected on the Advanced Guidance Control System (AGSC) mode panel, and the flight path selected via the Navigation Control/Display Unit (NCDU)
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