A Binary Variable Model for Affinity Propagation

Givoni, Inmar E.; Frey, Brendan J.

doi:10.1162/neco.2008.05-08-785

Cited by 22 publications

(40 citation statements)

References 2 publications

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“…We overcome these limitations by building our approach upon Affinity Propagation Clustering (APC), an exemplar-based clustering approach by Frey [9]. APC has been applied to a variety of problems [33][34][35][36] and extended in multiple ways. [37] uses hard cannot-link constraints between two data points which should not be in the same cluster.…”

Section: Related Workmentioning

confidence: 99%

Non-maximum Suppression for Object Detection by Passing Messages Between Windows

Rothe

Guillaumin

Gool

2015

Lecture Notes in Computer Science

137

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Abstract. Non-maximum suppression (NMS) is a key post-processing step in many computer vision applications. In the context of object detection, it is used to transform a smooth response map that triggers many imprecise object window hypotheses in, ideally, a single bounding-box for each detected object. The most common approach for NMS for object detection is a greedy, locally optimal strategy with several hand-designed components (e.g., thresholds). Such a strategy inherently suffers from several shortcomings, such as the inability to detect nearby objects. In this paper, we try to alleviate these problems and explore a novel formulation of NMS as a well-defined clustering problem. Our method builds on the recent Affinity Propagation Clustering algorithm, which passes messages between data points to identify cluster exemplars. Contrary to the greedy approach, our method is solved globally and its parameters can be automatically learned from training data. In experiments, we show in two contexts -object class and generic object detection -that it provides a promising solution to the shortcomings of the greedy NMS.

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Section: Related Workmentioning

confidence: 99%

Non-maximum Suppression for Object Detection by Passing Messages Between Windows

Rothe

Guillaumin

Gool

2015

Lecture Notes in Computer Science

137

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“…Our main contributions are to extend the previous work on APC [8,10,22] to multi-class detection and a large scale setting with thousands of fine-grained classes. Therefore, we incorporate a new constraint ensuring that each bounding box exemplar gets assigned only one label.…”

Section: Spatial Semantic Regularisermentioning

confidence: 99%

“…Max-sum message passing is applied to maximise equation (1) [8,10] consisting of two messages: The responsibility ρ ij (sent from i to j) describes how suited j would be as an exemplar for i. The availability α ij (sent from j to i) reflects the accumulated evidence for point i to choose point j as its exemplar:…”

Section: Standard Affinity Propagation Clusteringmentioning

confidence: 99%

Spatial Semantic Regularisation for Large Scale Object Detection

Mrowca

Rohrbach

Hoffman

et al. 2015

2015 IEEE International Conference on Computer Vision (ICCV)

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Large scale object detection with thousands of classes introduces the problem of many contradicting false positive detections, which have to be suppressed. Class-independent non-maximum suppression has traditionally been used for this step, but it does not scale well as the number of classes grows. Traditional non-maximum suppression does not consider label-and instance-level relationships nor does it allow an exploitation of the spatial layout of detection proposals. We propose a new multi-class spatial semantic regularisation method based on affinity propagation clustering [8,22], which simultaneously optimises across all categories and all proposed locations in the image, to improve both the localisation and categorisation of selected detection proposals. Constraints are shared across the labels through the semantic WordNet hierarchy. Our approach proves to be especially useful in large scale settings with thousands of classes, where spatial and semantic interactions are very frequent and only weakly supervised detectors can be built due to a lack of bounding box annotations. Detection experiments are conducted on the ImageNet and COCO dataset, and in settings with thousands of detected categories. Our method provides a significant precision improvement by reducing false positives, while simultaneously improving the recall.

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“…In order to make visible the influence of temperature on the measured natural frequencies, a powerful automatic clustering technique like affinity propagation (AP) algorithm can be applied [23,24], as here. AP identifies exemplars among data points and forms clusters of data points around these exemplars.…”

Section: Strategymentioning

confidence: 99%

“…The specific algorithm operates by simultaneously considering all data points as potential exemplars and exchanging messages between data points until a good set of exemplars and clusters emerges. More detailed information about the exact procedure on how the AP algorithm is passing the messages between data points can be found in [23,24].…”

Section: Strategymentioning

confidence: 99%

A Non‐linear Manifold Strategy for SHM Approaches

Dervilis

Antoniadou

Cross

et al. 2015

Strain

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In the data-based approach to structural health monitoring (SHM) when novelty detection is utilised as a means of diagnosis, benign operational and environmental variations of the structure can lead to false alarms and mask the presence of damage. The key element of this paper is to demonstrate a series of pattern recognition approaches which investigate complex correlations between the variables and thus potentially shed light on the variations within the data that are of interest for SHM.The non-linear manifold learning techniques discussed here, like locally linear embedding combined with robust discordance measures like the minimum covariance determinant and regression techniques like Gaussian processes offer a strategy that includes reliable novelty detection analysis but also a method of investigating the space where structural data clusters are lying.

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A Binary Variable Model for Affinity Propagation

Cited by 22 publications

References 2 publications

Non-maximum Suppression for Object Detection by Passing Messages Between Windows

Non-maximum Suppression for Object Detection by Passing Messages Between Windows

Spatial Semantic Regularisation for Large Scale Object Detection

A Non‐linear Manifold Strategy for SHM Approaches

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