Sparse Color Interest Points for Image Retrieval and Object Categorization

Stöttinger, Julian; Hanbury, Allan; Sebe, Nicu; Gevers, Theo

doi:10.1109/tip.2012.2186143

Cited by 43 publications

(41 citation statements)

References 62 publications

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“…[18], [22], and [23]. These articles report increased repeatability, entropy, and object categorization results as compared to intensity-based detections.…”

Section: A Related Workmentioning

confidence: 96%

Evaluation of Color Spatio-Temporal Interest Points for Human Action Recognition

Everts

Gemert

Gevers

2014

IEEE Trans. on Image Process.

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Abstract-This paper considers the recognition of realistic human actions in videos based on spatio-temporal interest points (STIPs). Existing STIP-based action recognition approaches operate on intensity representations of the image data. Because of this, these approaches are sensitive to disturbing photometric phenomena, such as shadows and highlights. In addition, valuable information is neglected by discarding chromaticity from the photometric representation. These issues are addressed by color STIPs. Color STIPs are multichannel reformulations of STIP detectors and descriptors, for which we consider a number of chromatic and invariant representations derived from the opponent color space. Color STIPs are shown to outperform their intensity-based counterparts on the challenging UCF sports, UCF11 and UCF50 action recognition benchmarks by more than 5% on average, where most of the gain is due to the multichannel descriptors. In addition, the results show that color STIPs are currently the single best low-level feature choice for STIP-based approaches to human action recognition.

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“…[18], [22], and [23]. These articles report increased repeatability, entropy, and object categorization results as compared to intensity-based detections.…”

Section: A Related Workmentioning

confidence: 96%

Evaluation of Color Spatio-Temporal Interest Points for Human Action Recognition

Everts

Gemert

Gevers

2014

IEEE Trans. on Image Process.

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“…It offers image classification and retrieval [3][4][5][6], object recognition and matching [7][8][9], 3D scene reconstruction [10], robot localization [11], object detection and tracking and video processing. All of these processing systems rely on the presence of stable and meaningful features in the image.…”

Section: Computer Vision Systemmentioning

confidence: 99%

Machine Learning framework for image classification

Loussaief¹,

Abdelkrim²

2018

Adv. sci. technol. eng. syst. j.

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Hereby in this paper, we are going to refer image classification. The main issue in image classification is features extraction and image vector representation. We expose the Bag of Features method used to find image representation. Class prediction accuracy of varying classifiers algorithms is measured on Caltech 101 images. For feature extraction functions we evaluate the use of the classical Speed Up Robust Features technique against global color feature extraction. The purpose of our work is to guess the best machine learning framework techniques to recognize the stop sign images.The trained model will be integrated into a robotic system in a future work.

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“…To begin, we will start developing our theory for spatiotemporal scale selection with respect to the problem of detecting sparse spatio-temporal interest points [6,9,11,14,18,20,21,30,49,88,94,97,99,100,107,122,124,126,127], which may be regarded as a conceptually simplest problem domain because of the sparsity of spatio-temporal interest points and the close connection between this problem domain and the detection of spatial interest points for which there exists a theoretically well-founded and empirically tested framework regarding scale selection over the spatial domain [1,4,5,15,17,25,42,65,72,74,84,89,90,112]. Specifically, using a non-causal Gaussian spatio-temporal scale-space model, we will perform a theoretical analysis of the spatio-temporal scale selection properties of eight different types of spatiotemporal interest point detectors and show that seven of them: (i) the spatial Laplacian of the first-order temporal derivative, (ii) the spatial Laplacian of the second-order temporal derivative, (iii) the determinant of the spatial Hessian of the first-order temporal derivative, (iv) the determinant of the spatial Hessian of the second-order temporal derivative, (v) the determinant of the spatio-temporal Hessian matrix, (vi) the first-order temporal derivative of the determinant of the spatial Hessian matrix and (vii) the second-order temporal derivative of the determinant of the spatial Hessian matrix, do all lead to fully scale-covariant spatio-temporal scale estimates and scale-invariant feature responses under independent scaling transformations of the spatial and the temporal domains.…”

Section: Fig 4 the First-and Second-order Temporal Derivatives Of Thmentioning

confidence: 99%

Spatio-Temporal Scale Selection in Video Data

Lindeberg

2017

J Math Imaging Vis

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This work presents a theory and methodology for simultaneous detection of local spatial and temporal scales in video data. The underlying idea is that if we process video data by spatio-temporal receptive fields at multiple spatial and temporal scales, we would like to generate hypotheses about the spatial extent and the temporal duration of the underlying spatio-temporal image structures that gave rise to the feature responses. For two types of spatio-temporal scale-space representations, (i) a non-causal Gaussian spatio-temporal scale space for offline analysis of pre-recorded video sequences and (ii) a time-causal and timerecursive spatio-temporal scale space for online analysis of real-time video streams, we express sufficient conditions for spatio-temporal feature detectors in terms of spatio-temporal receptive fields to deliver scale-covariant and scale-invariant feature responses. We present an in-depth theoretical analysis of the scale selection properties of eight types of spatio-temporal interest point detectors in terms of either: (i)-(ii) the spatial Laplacian applied to the first-and secondorder temporal derivatives, (iii)-(iv) the determinant of the spatial Hessian applied to the first-and second-order temporal derivatives, (v) the determinant of the spatio-temporal Hessian matrix, (vi) the spatio-temporal Laplacian and (vii)-(viii) the first-and second-order temporal derivatives of the determinant of the spatial Hessian matrix. It is shown that seven of these spatio-temporal feature detectors allow for provable scale covariance and scale invariance. Then, we describe a time-causal and time-recursive algorithm for detecting sparse spatio-temporal interest points from video streams and show that it leads to intuitively reasonable results. An experimental quantification of the accuracy of the spatio-temporal scale estimates and the amount of temporal delay obtained from these spatio-temporal interest point detectors is given, showing that: (i) the spatial and temporal scale selection properties predicted by the continuous theory are well preserved in the discrete implementation and (ii) the spatial Laplacian or the determinant of the spatial Hessian applied to the first-and second-order temporal derivatives leads to much shorter temporal delays in a timecausal implementation compared to the determinant of the spatio-temporal Hessian or the first-and second-order temporal derivatives of the determinant of the spatial Hessian matrix.

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Sparse Color Interest Points for Image Retrieval and Object Categorization

Cited by 43 publications

References 62 publications

Evaluation of Color Spatio-Temporal Interest Points for Human Action Recognition

Evaluation of Color Spatio-Temporal Interest Points for Human Action Recognition

Machine Learning framework for image classification

Spatio-Temporal Scale Selection in Video Data

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