Human action recognition by learning bases of action attributes and parts

Yao, Bowen; Jiang, Xiaoye; Khosla, Aditya; Lin, Andy; Guibas, Leonidas J.; Li, Feifei

doi:10.1109/iccv.2011.6126386

Cited by 509 publications

(452 citation statements)

References 29 publications

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“…Surprisingly, despite the simplicity of our approach which combines multiple fused color-shape models, the final performance significantly surpasses the state-of-the-art results on this large dataset. A significant gain of 6.2 in mean AP is achieved over the best results reported in the literature (Yao et al, 2011).…”

Section: Combining Fusion Techniques For Action Classificationsupporting

confidence: 60%

“…A mean AP of 62.0 is reported by Prest et al (2012) using a human-centric approach to localize humans and find object-human relationships. The best result of 65.1 is reported by Yao et al (2011) using a technique that learns a sparse basis of attributes and parts. Combining multiple fused color-shape representations using a classical bag-of-words framework without detection information provides comparable results to these more complex methods.…”

Section: Combining Fusion Techniques For Action Classificationmentioning

confidence: 97%

“…It is worth mentioning that the color-based models are complementary to detection-based techniques and the two approaches can be combined to further improve action recognition performance. Table 5 shows a comparison with state-of-the-art performance reported on the Stanford-40 dataset (Yao et al, 2011;Li et al, 2010;Wang et al, 2010). In order to improve the overall performance on this large dataset we increase the vocabulary size for shape to 4000.…”

Section: Combining Fusion Techniques For Action Classificationmentioning

confidence: 99%

“…Action recognition in static images has gained a lot of attention recently (Sharma et al, 2012;Prest et al, 2012;Delaitre et al, 2010;Yao and Li, 2012;Yao et al, 2011;Maji et al, 2011). Recognizing human actions in static images is difficult due to the lack of temporal information and to large variations in human appearance and pose.…”

Section: Related Workmentioning

confidence: 99%

“…They further propose a discriminative learning procedure to solve the problem of the large number of possible interaction pairs. Yao et al (2011) propose to use attributes and parts by learning a set of sparse attribute and part bases for action recognition. The approach we propose in this paper is complementary to the aforementioned techniques and can be used in combination with any of them to improve action recognition.…”

Section: Related Workmentioning

confidence: 99%

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Coloring Action Recognition in Still Images

et al. 2013

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In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bagof-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images.We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color-shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification.

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Section: Combining Fusion Techniques For Action Classificationsupporting

confidence: 60%

Section: Combining Fusion Techniques For Action Classificationmentioning

confidence: 97%

Section: Combining Fusion Techniques For Action Classificationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Coloring Action Recognition in Still Images

et al. 2013

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Image Pre-Processing

Krig

2014

Computer Vision Metrics

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This chapter describes the methods used to prepare images for further analysis, including interest point and feature extraction. Some of these methods are also useful for global and local feature description, particularly the metrics derived from transforms and basis spaces. The focus is on image pre-processing for computer vision, so we do not cover the entire range of image processing topics applied to areas such as computational photography and photo enhancements, so we refer the interested reader to various other standard resources in Digital Image Processing and Signal Processing as we go along [4,9,325,326], and we also point out interesting research papers that will enhance understanding of the topics. Note ■ Readers with a strong background in image processing may benefit from a light reading of this chapter. Perspectives on Image Processing Image processing is a vast field that cannot be covered in a single chapter. So why do we discuss image pre-processing in a book about computer vision? The reason is to advance the science of local and global feature description, as image pre-processing is typically ignored in discussions of feature description. Some general image processing topics are covered here in light of feature description, intended to illustrate rather than to proscribe, as applications and image data will guide the image pre-processing stage. Some will argue that image pre-processing is not a good idea, since it distorts or changes the true nature of the raw data. However, intelligent use of image pre-processing can provide benefits and solve problems that ultimately lead to better local and global feature detection. We survey common methods for image enhancements and corrections that will affect feature analysis downstream in the vision pipeline in both favorable and unfavorable ways, depending on how the methods are employed.

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Global and Regional Features

Krig

2014

Computer Vision Metrics

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This chapter covers the metrics of general feature description, often used for whole images and image regions, including textural, statistical, model based, and basis space methods. Texture, a key metric, is a well-known topic within image processing, and it is commonly divided into structural and statistical methods. Structural methods look for features such as edges and shapes, while statistical methods are concerned with pixel value relationships and statistical moments. Methods for modeling image texture also exist, primarily useful for image synthesis rather than for description. Basis spaces, such as the Fourier space, are also use for feature description. It is difficult to develop clean partitions between the related topics in image processing and computer vision that pertain to global vs. regional vs. local feature metrics; there is considerable overlap in the applications of most metrics. However, for this chapter, we divide these topics along reasonable boundaries, though those borders may appear to be arbitrary. Similarly, there is some overlap between discussions here on global and regional features and topics that were covered in Chapter 2 on image processing and that will be discussed in Chapter 6 on local features. In short, many methods are used for local, regional, and global feature description, as well as image processing, such as the Fourier transform and the LBP. But we begin with a brief survey of some key ideas in the field of texture analysis and general vision metrics. Historical Survey of Features To compare and contrast global, regional, and local feature metrics, it is useful to survey and trace the development of the key ideas, approaches, and methods used to describe features for machine vision. This survey includes image processing (textures and statistics) and machine vision (local, regional, and global features). Historically, the choice of feature metrics was limited to those that were computable at the time, given the limitations in compute performance, memory, and sensor technology. As time passed and technology • Transform or basis based, including methods such as Fourier, wavelets, Gabor filters, Zernike, and other basis spaces, which are treated here as a sub-class of the statistical methods (statistical moments); however, basis spaces are used in transforms for image processing and filtering as well.

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Human action recognition by learning bases of action attributes and parts

Cited by 509 publications

References 29 publications

Coloring Action Recognition in Still Images

Coloring Action Recognition in Still Images

Image Pre-Processing

Global and Regional Features

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