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Many novel multimedia systems and applications use visual sensor arrays. An important issue in designing sensor arrays is the appropriate placement of the visual sensors such that they achieve a predefined goal. In this paper we focus on the placement with respect to maximizing coverage or achieving coverage at a certain resolution. We identify and consider four different problems: maximizing coverage subject to a given number of cameras (a) or a maximum total price of the sensor array (b),doi:10.1016/b978-0-12-374633-7.00007-0 fatcat:fgxzgofctneklk3qpmtfa4c5e4
more »... mizing camera poses given fixed locations (c), and minimizing the cost of a sensor array given a minimally required percentage of coverage (d). To solve these problems, we propose different algorithms. Our approaches can be subdivided into algorithms which give a global optimum solution and heuristics which solve the problem within reaonable time and memory consumption at the cost of not necessarily determining the global optimum. We also present a user-interface to enter and edit the spaces under analysis, the optimization problems as well as the other setup parameters. The different algorithms are experimentally evaluated and results are presented. The results show that the algorithms work well and are suited for different practical applications. For the final paper it is planned to have the user interface running as a web service.
In this work, we extend the standard single-layer probabilistic Latent Semantic Analysis (pLSA) (Hofmann in Mach Learn 42(1-2):177-196, 2001) to multiple layers. As multiple layers should naturally handle multiple modalities and a hierarchy of abstractions, we denote this new approach multilayer multimodal probabilistic Latent Semantic Analysis (mm-pLSA). We derive the training and inference rules for the smallest possible non-degenerated mm-pLSA model: a model with two leaf-pLSAs and a singledoi:10.1007/s13735-012-0006-4 fatcat:usa7xvlgrzfdxoenf3ovs6hqle
more »... op-level pLSA node merging the two leaf-pLSAs. We evaluate this approach on two pairs of different modalities: SIFT features and image annotations (tags) as well as the combination of SIFT and HOG features. We also propose a fast and strictly stepwise forward procedure to initialize the bottom-up mm-pLSA model, which in turn can then be post-optimized by the general mm-pLSA learning algorithm. The proposed approach is evaluated in a query-by-example retrieval task where various variants of our mm-pLSA system are compared to systems relying on a single modality and other ad-hoc combinations of feature histograms. We further describe possible pitfalls of the mm-pLSA training and analyze the resulting model yielding an intuitive explanation of its behaviour.
Many novel multimedia, home entertainment, visual surveillance and health applications use multiple audio-visual sensors. We present a novel approach for position and pose calibration of visual sensors, i.e. cameras, in a distributed network of general purpose computing devices (GPCs). It complements our work on position calibration of audio sensors and actuators in a distributed computing platform  . The approach is suitable for a wide range of possible -even mobile -setups since (a)doi:10.1007/s00530-006-0057-6 fatcat:s6zpusuedraa7dpfjgcun73owu
more »... onization is not required, (b) it works automatically, (c) only weak restrictions are imposed on the positions of the cameras, and (d) no upper limit on the number of cameras and displays under calibration is imposed. Corresponding points across different camera images are established automatically. Cameras do not have to share one common view. Only a reasonable overlap between camera subgroups is necessary. The method has been sucessfully tested in numerous multi-camera environments with a varying number of cameras and has proven itself to work extremely accurate. Once all distributed visual sensors are calibrated, we focus on post-optimizing their poses to increase coverage of the space observed. A linear programming approach is derived that determines jointly for each camera the pan and tilt angle that maximizes the coverage of the space at a given sampling frequency. Experimental results clearly demonstrate the gain in visual coverage. f or 0 ≤ i ≤ (sx −1), 0 ≤ j ≤ (sy −1), 0 ≤ k ≤ (sz −1),
In the paper, we propose and test an unsupervised approach for image ranking. Prior solutions are based on image content and the similarity graph connecting images. We generalize this idea by directly estimating the likelihood of each photo in a feature space. We hypothesize the photos at the peaks of this distribution are the most likely photos for any given category and therefore these images are the most representative. Our approach is unsupervised and allows for various feature modalities.doi:10.1145/1631058.1631074 dblp:conf/mm/HorsterSRW09 fatcat:anvxwshxnbfvhbochtjezngjbm
more »... e demonstrate the effectiveness of our approach using both visual-content-based and tag-based features. The experimental evaluation shows that the presented model outperforms baseline approaches. Moreover, the performance of our method will only get better with time as more images move online and it is thus possible to build more detailed models based on the approach presented here.
Online image repositories such as Flickr contain hundreds of millions of images and are growing quickly. Along with that the needs for supporting indexing, searching and browsing is becoming more and more pressing. Here we will employ the image content as a source of information to retrieve images and study the representation of images by topic models for content-based image retrieval. We focus on incorporating different types of visual descriptors into the topic modeling context. Threedoi:10.1109/cvpr.2007.383490 dblp:conf/cvpr/HorsterL07 fatcat:arm3opexqnhrph2lteel4r6tfe
more »... t fusion approaches are explored. The image representations for each fusion approach are learned in an unsupervised fashion, and each image is modeled as a mixture of topics/object parts depicted in the image. However, not all object classes will benefit from all visual descriptors. Therefore, we also investigate which visual descriptor (set) is most appropriate for each of the twelve classes under consideration. We evaluate the presented models on a real world image database consisting of more than 246,000 images.
With the explosion of the number of images in personal and on-line collections, efficient techniques for navigating, indexing, labeling and searching images become more and more important. In this work we will rely on the image content as the main source of information to retrieve images. We study the representation of images by topic models in its various aspects and extend the current models. Starting from a bag-of-visual-words image description based on local image features, imagesdoi:10.1145/1738921.1738925 fatcat:ia42ztkmznfdvmsowo2uo6ufda
more »... tions are learned in an unsupervised fashion and each image is modeled as a mixture of topics/object parts depicted in the image. Thus topic models allow us to automatically extract high-level image content descriptions which in turn can be used to find similar images. Further, the typically low-dimensional topic-model-based representation enables efficient and fast search, especially in very large databases. In this thesis we present a complete image retrieval system based on topic models and evaluate the suitability of different types of topic models for the task of large-scale retrieval on real-world databases. Different similarity measure are evaluated in a retrieval-by-example task. Next, we focus on the incorporation of different types of local image features in the topic models. For this, we first evaluate which types of feature detectors and descriptors are appropriate to model the images, then we propose and explore models that fuse multiple types of local features. All basic topic models require the quantization of the otherwise high-dimensional continuous local feature vectors into a finite, discrete vocabulary to enable the bag-of-words image representation the topic models are built on. As it is not clear how to optimally quantize the high-dimensional features, we introduce different extensions to a basic topic model which model the visual vocabulary continuously, making the quantization step obsolete. On-line image repositories of the Web 2.0 often store additional information about the images besides their pixel values, called metadata, such as associated tags, date of creation, ownership and camera parameters. In this work we also investigate how to include such cues in our retrieval system. We present work in progress on (hierarchical) models which fuse features from multiple modalities. Finally, we present an approach to find the most relevant images, i.e., very representative images, in a large web-scale collection given a query term. Our unsupervised approach ranks highest the image whose image content and its various metadata types gives us the highest probability according to a the model we automatically build for this tag. Throughout this thesis, the suitability of all proposed models and approaches is demonstrated by user studies on a real-world, large-scale database in the context of image retrieval tasks. We use databases consisting of more than 240,000 images which have been downloaded from the public Flickr repository.
This work studies a new approach for image retrieval on largescale community databases. Our proposed system explores two different modalities: visual features and communitygenerated metadata, such as tags. We use topic models to derive a high-level representation appropriate for retrieval for each of our images in the database. We evaluate the proposed approach experimentally in a query-by-example retrieval task and compare our results to systems relying solely on visual features or tagdoi:10.1109/icme.2009.5202522 dblp:conf/icmcs/RombergHL09 fatcat:kfremgkw3bgq3kn3mriwutqa3q
more »... . It is shown that the proposed multimodal system outperforms the unimodal systems by approximately 36%.
It is current state of knowledge that our neocortex consists of six layers  . We take this knowledge from neuroscience as an inspiration to extend the standard single-layer probabilistic Latent Semantic Analysis (pLSA)  to multiple layers. As multiple layers should naturally handle multiple modalities and a hierarchy of abstractions, we denote this new approach multilayer multimodal probabilistic Latent Semantic Analysis (mm-pLSA). We derive the training and inference rules for thedoi:10.1145/1646396.1646408 dblp:conf/civr/LienhartRH09 fatcat:akhc5cbl3faflj54btxlfor4py
more »... est possible non-degenerated mm-pLSA model: a model with two leaf-pLSAs (here from two different data modalities: image tags and visual image features) and a single top-level pLSA node merging the two leaf-pLSAs. From this derivation it is obvious how to extend the learning and inference rules to more modalities and more layers. We also propose a fast and strictly stepwise forward procedure to initialize bottom-up the mm-pLSA model, which in turn can then be post-optimized by the general mm-pLSA learning algorithm. We evaluate the proposed approach experimentally in a query-by-example retrieval task using 50dimensional topic vectors as image models. We compare various variants of our mm-pLSA system to systems relying solely on visual features or tag features and analyze possible pitfalls of the mm-pLSA training. It is shown that the best variant of the the proposed mm-pLSA system outperforms the unimodal systems by approximately 19% in our query-by-example task.
Searching for relevant images given a query term is an important task in nowadays large-scale community databases. The image ranking approach presented in this work represents an image collection as a graph that is built using a multimodal similarity measure based on visual features and user tags. We perform a random walk on this graph to find the most common images. Further we discuss several scalability issues of the proposed approach and show how in this framework queries can be answereddoi:10.1145/1743384.1743402 dblp:conf/mir/RichterRHL10 fatcat:ylplqntbq5go7nm25wrcfvi35i
more »... . Experimental results validate the effectiveness of the presented algorithm.
Online image repositories such as Flickr contain hundreds of millions of images and are growing quickly. Along with that the needs for supporting indexing, searching and browsing is becoming more and more pressing. In this work we will employ the image content as a source of information to retrieve images. We study the representation of images by Latent Dirichlet Allocation (LDA) models for content-based image retrieval. Image representations are learned in an unsupervised fashion, and eachdoi:10.1145/1282280.1282283 dblp:conf/civr/HorsterLS07 fatcat:av6ycxhv3bhffamp4wmatrwn4m
more »... e is modeled as the mixture of topics/object parts depicted in the image. This allows us to put images into subspaces for higher-level reasoning which in turn can be used to find similar images. Different similarity measures based on the described image representation are studied. The presented approach is evaluated on a real world image database consisting of more than 246,000 images and compared to image models based on probabilistic Latent Semantic Analysis (pLSA). Results show the suitability of the approach for large-scale databases. Finally we incorporate active learning with user relevance feedback in our framework, which further boosts the retrieval performance.
Topic models such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) have been shown to perform well in various image content analysis tasks. However, due to the origin of these models from the text domain, almost all prior work uses discrete vocabularies even when applied in the image domain. Thus in these works the continuous local features used to describe an image need to be quantized to fit the model. In this work we will propose and evaluate threedoi:10.1145/1386352.1386395 dblp:conf/civr/HorsterLS08 fatcat:m6lz4trytbf6xowgm4fy2btzca
more »... ent extensions to the pLSA framework so that words are modeled as continuous feature vector distributions rather than crudely quantized high-dimensional descriptors. The performance of these continuous vocabulary models are compared in an automatic scene recognition task. Our experiments clearly show that the continuous approaches outperform the standard pLSA model. In this paper all required equations for parameter estimation and inference are given for each of the three models.
Lecture Notes in Computer Science
Annals of Neurology
.), Studien zum vorhellenistischen und hellenistischen Herrscherkult. 5 3 7 Marietta Horster (Mainz), 542 María Paz de Hoz (Salamanca), Anne Gangloff (Hg.), Médiateurs culturels et politiques dans l'Empire ... Études sur le monde du travail dans les sociétés urbaines de l'empire romain. 5 4 6 Manuel Flecker (Tübingen), Eva-Maria Lackner, Republikanische Fora. 5 4 8 Harriet Isabel Flower (Princeton), 5 1 7 Karl-Joachim ...doi:10.1524/klio.2013.95.2.i fatcat:extepamnlfcxbjppxeepzu6hi4
P., ann Horster, B. H. J. Studies on w 5 1. Methods of estimation, purification a1 g enzyme irch. Riochem 18, 9 (1948 37. Sincer, T. P., ann Horster, B. H. J. Studies Il. Kinetics rch. ... ., 27, 191 (1951 Eva, W. J., Geppes, W. F., anno Frisert, B. A of various methods of measuring flour gassing ve! erea Chem., 14, 458 (1937) Fisuer, FE. A., Hatton, P., ann Hines, S. ...
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