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Improving VLAD: Hierarchical coding and a refined local coordinate system

Improving VLAD: Hierarchical coding and a refined local coor...

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作者： Eggert, Christian Romberg, Stefan Lienhart, Rainer Multimedia Computing and Computer Vision Lab University of Augsburg Germany

ISBN: (纸本)9781479957514

The enormous growth of image databases calls for new techniques for fast and effective image search that scales with millions of images. Most importantly, the setting requires a compact but also descriptive image signature. Recently, the vector of aggregated local descriptors (VLAD) [1] has received much attention in large-scale image retrieval. In this paper we present two modifications for VLAD which improve the retrieval performance of the signature. © 2014 IEEE.

关键词： Image retrieval

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vision substitution experiments with see ColOr

Vision substitution experiments with see ColOr

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5th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2013

作者： Bologna, Guido Gomez, Juan Diego Pun, Thierry Computer Vision and Multimedia Lab. University of Geneva Switzerland

ISBN: (纸本)9783642386367

See ColOr is a mobility aid for visually impaired people that uses the auditory channel to represent portions of captured images in real time. A distinctive feature of the See ColOr interface is the simultaneous coding of colour and depth. Four main modules were developed, in order to replicate a number of mechanisms present in the human visual systems. In this work, we first present the main experiments carried out in the first years of the project;among them : the avoidance of obstacles, the recognition and localization of objects, the detection of edges and the identification of coloured targets. Finally, we introduce new undergoing experiments in Colombia with blind persons, whose purpose is (1) to determine and to touch a target;(2) to navigate and to find a person;and (3) to find particular objects. Preliminary results illustrate encouraging results. © 2013 Springer-Verlag.

关键词： Handicapped persons

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OBJECT DETECTION USING HIERARCHICAL GRAPH-BASED SEGMENTATION

OBJECT DETECTION USING HIERARCHICAL GRAPH-BASED SEGMENTATION

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kim, Jungho Choi, Byeongho Kweon, In-So Multimedia IP Research Center KETI Robotics and Computer Vision Lab. KAIST

ISBN: (纸本)9781479903566

Object detection in real images or videos is challenging because the shapes and sizes of objects vary significantly according to their poses, camera viewing direction, and partial occlusion. Previous detection methods employ sliding-window-based schemes that scan windows across an image, requiring many differently shaped windows to capture shape and size variation. In order to solve this problem, we propose an object detection method using hierarchical graph-based segmentation: color-consistent parts are obtained by part-level segmentation and category-consistent regions are found using object-level segmentation. Thus we can avoid scanning a lot of windows across whole images by using part-level segmentation and robustly detect the objects of various shapes and sizes by using object-level segmentation. In addition, we evaluate detection performance using various classifiers with our detection approach.

关键词： Object detection Object classification Graph-based segmentation

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Pose estimation for deriving kinematic parameters of competitive swimmers

Pose estimation for deriving kinematic parameters of competi...

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computer vision Applications in Sports 2017

作者： Zecha, Dan Eggert, Christian Lienhart, Rainer Multimedia Computing and Computer Vision Lab. Augsburg University Germany

In the field of competitive swimming a quantitative evaluation of kinematic parameters is a valuable tool for coaches but also a labor intensive task. We present a system which is able to automate the extraction of many kinematic parameters such as stroke frequency, kick rates and stroke-specific intra-cyclic parameters from video footage of an athlete. While this task can in principle be solved by human pose estimation, the problem is exacerbated by permanently changing self-occlusion and severe noise caused by air bubbles, splashes, light reflection and light refraction. Current approaches for pose estimation are unable to provide the necessary localization precision under these conditions in order to enable accurate estimates of all desired kinematic parameters. In this paper we reduce the problem of kinematic parameter derivation to detecting key frames with a deep neural network human pose estimator. We show that we can correctly detect key frames with a precision which is on par with the human annotation performance. From the correctly located key frames, aforementioned parameters can be successfully inferred. © 2017, Society for Imaging Science and Technology.

关键词： Deep neural networks

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Robust feature bundling

Robust feature bundling

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13th Pacific-Rim Conference on multimedia, PCM 2012

作者： Romberg, Stefan August, Moritz Ries, Christian X. Lienhart, Rainer Multimedia Computing and Computer Vision Lab. Augsburg University Germany

ISBN: (纸本)9783642347771

In this work we present a feature bundling technique that aggregates individual local features with features from their spatial neighborhood into bundles. The resulting bundles carry more information of the underlying image content than single visual words. As in practice an exact search for such bundles is infeasible, we employ a robust approximate similarity search with min-hashing in order to retrieve images containing similar bundles. We demonstrate the benefits of these bundles for small object retrieval, i.e. logo recognition, and generic image retrieval. Multiple bundling strategies are explored and thoroughly evaluated on three different datasets. © 2012 Springer-Verlag.

关键词： Image retrieval

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Bundle min-hashing for logo recognition 13

Bundle min-hashing for logo recognition

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3rd ACM International Conference on multimedia Retrieval, ICMR 2013

作者： Romberg, Stefan Lienhart, Rainer Multimedia Computing and Computer Vision Lab. Augsburg University Augsburg Germany

ISBN: (纸本)9781450320337

We present a scalable logo recognition technique based on feature bundling. Individual local features are aggregated with features from their spatial neighborhood into bundles. These bundles carry more information about the image content than single visual words. The recognition of logos in novel images is then performed by querying a database of reference images. We further propose a novel WGC-constrained RANSAC and a technique that boosts recall for object retrieval by synthesizing images from original query or reference images. We demonstrate the benefits of these techniques for both small object retrieval and logo recognition. Our logo recognition system clearly outperforms the current state-of-the-art with a recall of 83% at a precision of 99%. © 2013 ACM.

关键词： Query processing

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Registration of brain MR images using feature information of structural elements

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6th Pacific Rim Conference on multimedia - Advances in Mulitmedia Information Processing - PCM 2005

作者： Chae, Jeong-Sook Cho, Hyung-Jea Korea Republic of Computer Vision and Multimedia Lab. Department of Computer Engineering Dongguk University Korea Republic of

ISBN: (纸本)3540300279

In this paper, we propose a new registration algorithm, which can provide more exact criteria for deciphering brain diseases. At the first stage, our algorithm divides the areas of the brain structures to extract their features. After calculating contour and area information, the grouping step is performed. At the next stage, the brain structures are precisely classified with respect to the shape of cerebrospinal fluid and the volume of brain structures. These features are finally integrated into a knowledge base to build up a new standard atlas for normal brain MR images. Using this standard atlas, we perform the registration process after extracting the brain structures from the MR image to be compared. Finally, we analyze the registration results of the normal and abnormal MR images, and showed that the exactness of our algorithm is relatively superior to the previous methods. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Medical imaging

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Visual object categorization using distance-based discriminant analysis

Visual object categorization using distance-based discrimina...

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2004 IEEE computer Society Conference on computer vision and Pattern Recognition Workshops, CVPRW 2004

作者： Kosinov, Serhiy Marchand-Maillet, Stéphane Pun, Thierry Computer Vision and Multimedia Lab University of Geneva 24 rue du General-Dufour Geneva Switzerland

This paper formulates the problem of object categorization in the discriminant analysis framework focusing on transforming visual feature data so as to make it conform to the compactness hypothesis in order to improve categorization accuracy. The sought transformation, in turn, is found as a solution to an optimization problem formulated in terms of inter-observation distances only, using the technique of iterative majorization. The proposed approach is suitable for both binary and multiple-class categorization problems, and can be applied as a dimensionality reduction technique. In the latter case, the number of discriminative features is determined automatically since the process of feature extraction is fully embedded in the optimization procedure. Performance tests validate our method on a number of benchmark data sets from the UCI repository, while the experiments in the application of visual object and content-based image categorization demonstrate very competitive results, asserting the method's capability of producing semantically relevant matches that share the same or synonymous vocabulary with the query category and allowing multiple pertinent category assignment. © 2004 IEEE.

关键词： Discriminant analysis

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Computing the reeb graph for triangle mesheswith active contours

Computing the reeb graph for triangle mesheswith active cont...

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1st International Conference on Pattern Recognition Applications and Methods, ICPRAM 2012

作者： Brandolini, Laura Piastra, Marco Computer Vision and Multimedia Lab Università degli Studi di Pavia via Ferrata 1 27100 Pavia Italy

ISBN: (纸本)9789898425980

This paper illustrates a novel method to compute the Reeb graph for triangle meshes. The algorithm is based on the definition of discrete, active contours as counterparts of continuous level lines. Active contours are made up of edges and vertices with multiple presence and implicitly maintain a faithful representation of the level lines, even in case of coarse meshes with higher genus. This approach gives a great advantage in the identification of the nodes in the Reeb graph and also improves the overall efficiency of the algorithm in that at each step only the information local to the contours and their immediate neighborhood needs to be processed. The validation of functional integrity for the algorithm has been carried out experimentally, with real-world data, without mesh pre-processing.

关键词： Mesh generation

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From mid-level to high-level: Semantic inference for multimedia retrieval

From mid-level to high-level: Semantic inference for multime...

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5th International Workshop on Semantic Media Adaptation and Personalization, SMAP 2010

作者： Zhang, Qianni Izquierdo, Ebroul Multimedia and Vision Research Lab. School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

ISBN: (纸本)9781424486007

The problem of bridging the semantic gap can be approached by dividing all types of metadata extracted from multimedia content into three levels - low, mid and high - according to their levels of semantic abstraction and try to define the mapping between them. This paper proposes a scheme for extracting high-level semantic information out of mid-level features, which can be applied in dealing with highly semantic queries in image retrieval. Mid-level features used in this research contain some level of semantic meaning but are not directly useful in real retrieval scenarios. However, they usually have strong relationships to high-level queries but these relationships are often ignored due to their implicitness. The aim of the proposed approach is to explore hidden interrelationships between midlevel features and the high-level query terms, by learning a Bayesian network model from a small amount of training data. Semantic inference and reasoning is then carried out based on the learned Bayesian network model, in order to decide whether a video is relevant to a high-level query. The extracted high-level semantic terms can be annotated on the video content for future retrieval. Two experimental scenarios were considered in this paper and the experiments on RUSHES videos have produced satisfactory results. © 2010 IEEE.

关键词： Semantics

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