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Journal ArticleDOI

Mean shift: a robust approach toward feature space analysis

01 May 2002-IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE Computer Society)-Vol. 24, Iss: 5, pp 603-619

TL;DR: It is proved the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density.
Abstract: A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.
Topics: Mean-shift (63%), Smoothing (58%), Estimator (56%), Kernel (statistics) (56%), Feature vector (56%)
Citations
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Journal ArticleDOI
Radhakrishna Achanta1, Appu Shaji1, Kevin Smith2, Aurelien Lucchi  +2 moreInstitutions (2)
TL;DR: A new superpixel algorithm is introduced, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels and is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.
Abstract: Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels. Despite its simplicity, SLIC adheres to boundaries as well as or better than previous methods. At the same time, it is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.

6,470 citations


Cites background from "Mean shift: a robust approach towar..."

  • ...Superpixels are created by minimizing a cost function defined over the graph....

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Proceedings ArticleDOI
07 Jul 2001
Abstract: This paper presents a database containing 'ground truth' segmentations produced by humans for images of a wide variety of natural scenes. We define an error measure which quantifies the consistency between segmentations of differing granularities and find that different human segmentations of the same image are highly consistent. Use of this dataset is demonstrated in two applications: (1) evaluating the performance of segmentation algorithms and (2) measuring probability distributions associated with Gestalt grouping factors as well as statistics of image region properties.

6,077 citations


Journal ArticleDOI
Alper Yilmaz1, Omar Javed, Mubarak Shah2Institutions (2)
TL;DR: The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends to discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.
Abstract: The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.

5,085 citations


Cites background or methods from "Mean shift: a robust approach towar..."

  • ...Mean-shift clustering is scalable to various other applications such as edge detection, image regularization [Comaniciu and Meer 2002], and tracking [Comaniciu et al. 2003]....

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  • ...Use of concentric circles implicitly encodes the spatial information which in regular histogram is only possible when the spatial (x, y) coordi­nates are included in the observation vector [Comaniciu and Meer 2002]....

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  • ...Use of concentric circles implicitly encodes the spatial information which in regular histogram is only possible when the spatial (x, y) coordinates are included in the observation vector [Comaniciu and Meer 2002]....

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  • ...Mean-shift clustering is scalable to various other applications such as edge detection, image regularization [Comaniciu and Meer 2002], and tracking [Comaniciu et al....

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Journal ArticleDOI
TL;DR: A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed, which employs a metric derived from the Bhattacharyya coefficient as similarity measure, and uses the mean shift procedure to perform the optimization.
Abstract: A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking.

4,901 citations


Cites background from "Mean shift: a robust approach towar..."

  • ...Kernelswith Epanechnik ov profile [17] 4_^<#I dY¬ ­ ‹ “ ’ # i8r6®^<# if ^° ̄M8 – otherwise (12)...

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  • ...Sinceour distancefunction is smooth,theprocedureusesgradientinformationwhich is providedby themeanshift vector[17]....

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Journal ArticleDOI
TL;DR: This paper introduces selective search which combines the strength of both an exhaustive search and segmentation, and shows that its selective search enables the use of the powerful Bag-of-Words model for recognition.
Abstract: This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html ).

4,840 citations


Cites background from "Mean shift: a robust approach towar..."

  • ...Bottom-up grouping is a popular approach to segmentation [6, 13], hence we adapt it for selective search....

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  • ...Research on this topic has yielded tremendous progress over the past years [3, 6, 13, 26]....

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References
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Journal ArticleDOI
Pietro Perona1, Jitendra Malik1Institutions (1)
TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.
Abstract: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing. It is shown that the 'no new maxima should be generated at coarse scales' property of conventional scale space is preserved. As the region boundaries in the approach remain sharp, a high-quality edge detector which successfully exploits global information is obtained. Experimental results are shown on a number of images. Parallel hardware implementations are made feasible because the algorithm involves elementary, local operations replicated over the image. >

11,917 citations


"Mean shift: a robust approach towar..." refers background in this paper

  • ...There are a large variety of approaches to achieve this goal, from adaptive Wi ener filtering [31], to implementing isotropic [50] and anisotropic [44] local diffusion processes , a topic which recently received renewed interest [19, 37, 56]....

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Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
202213
2021399
2020512
2019633
2018644
2017693