557 research outputs found

    A generalized probabilistic framework for compact codebook creation

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    Appearing in IEEE Conf. Comp. Vis. Pattern Recogn. 2011. This reprint differs from the original in pagination and typographic detailCompact and discriminative visual codebooks are pre-ferred in many visual recognition tasks. In the literature, a few researchers have taken the approach of hierarchically merging visual words of a initial large-size code-book, but implemented this idea with different merging cri- teria. In this work, we show that by defining different class-conditional distribution functions and parameter estimation methods, these merging criteria can be unified under a single probabilistic framework. More importantly, by adopting new distribution functions and/or parameter estimation methods, we can generalize this framework to produce a spectrum of novel merging criteria. Two of them are particularly focused in this work. For one criterion, we adopt the multinomial distribution to model each object class, and for the other criterion we propose a large-margin based parameter estimation method. Both theoretical analysis and experimental study demonstrate the superior performance of the two new merging criteria and the general applicability of our probabilistic framework.Lingqiao Liu, Lei Wang and Chunhua Shenhttp://cvpr2011.org/index.htm

    A quick search method for audio signals based on a piecewise linear representation of feature trajectories

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    This paper presents a new method for a quick similarity-based search through long unlabeled audio streams to detect and locate audio clips provided by users. The method involves feature-dimension reduction based on a piecewise linear representation of a sequential feature trajectory extracted from a long audio stream. Two techniques enable us to obtain a piecewise linear representation: the dynamic segmentation of feature trajectories and the segment-based Karhunen-L\'{o}eve (KL) transform. The proposed search method guarantees the same search results as the search method without the proposed feature-dimension reduction method in principle. Experiment results indicate significant improvements in search speed. For example the proposed method reduced the total search time to approximately 1/12 that of previous methods and detected queries in approximately 0.3 seconds from a 200-hour audio database.Comment: 20 pages, to appear in IEEE Transactions on Audio, Speech and Language Processin

    Probabilistic Integration of Intensity and Depth Information for Part-Based Vehicle Detection

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    International audienceIn this paper, an object class recognition method is presented. The method uses local image features and follows the part-based detection approach. It fuses intensity and depth information in a probabilistic framework. The depth of each local feature is used to weigh the probability of finding the object at a given distance. To train the system for an object class, only a database of images annotated with bounding boxes is required, thus automatizing the extension of the system to different object classes. We apply our method to the problem of detecting vehicles from a moving platform. The experiments with a data set of stereo images in an urban environment show a significant improvement in performance when using both information modalities

    Swarm-Organized Topographic Mapping

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    Topographieerhaltende Abbildungen versuchen, hochdimensionale oder komplexe Datenbestände auf einen niederdimensionalen Ausgaberaum abzubilden, wobei die Topographie der Daten hinreichend gut wiedergegeben werden soll. Die Qualität solcher Abbildung hängt gewöhnlich vom eingesetzten Nachbarschaftskonzept des konstruierenden Algorithmus ab. Die Schwarm-Organisierte Projektion ermöglicht eine Lösung dieses Parametrisierungsproblems durch die Verwendung von Techniken der Schwarmintelligenz. Die praktische Verwendbarkeit dieser Methodik wurde durch zwei Anwendungen auf dem Feld der Molekularbiologie sowie der Finanzanalytik demonstriert
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