Discovering Primary Objects in Videos by Saliency Fusion and Iterative Appearance Estimation

Yang, Jiong; Zhao, Gangqiang; Yuan, Junsong; Shen, Xiaohui; Lin, Zhe; Price, Brian; Brandt, Jonathan

doi:10.1109/tcsvt.2015.2433171

Cited by 34 publications

(31 citation statements)

References 54 publications

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“…This is suitable for targeted object discovery that is tailored to users' interests. In addition, our model can be combined with co-segmentation algorithms [47] and visual saliency discovery [48].…”

Section: Discussionmentioning

confidence: 99%

Topical video object discovery from key frames by modeling word co-occurrence prior

Zhao

Yuan

Hua

et al. 2015

IEEE Trans. on Image Process.

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A topical video object refers to an object, that is, frequently highlighted in a video. It could be, e.g., the product logo and the leading actor/actress in a TV commercial. We propose a topic model that incorporates a word co-occurrence prior for efficient discovery of topical video objects from a set of key frames. Previous work using topic models, such as latent Dirichelet allocation (LDA), for video object discovery often takes a bag-of-visual-words representation, which ignored important co-occurrence information among the local features. We show that such data driven co-occurrence information from bottom-up can conveniently be incorporated in LDA with a Gaussian Markov prior, which combines top-down probabilistic topic modeling with bottom-up priors in a unified model. Our experiments on challenging videos demonstrate that the proposed approach can discover different types of topical objects despite variations in scale, view-point, color and lighting changes, or even partial occlusions. The efficacy of the co-occurrence prior is clearly demonstrated when compared with topic models without such priors.

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Section: Discussionmentioning

confidence: 99%

Topical video object discovery from key frames by modeling word co-occurrence prior

Zhao

Yuan

Hua

et al. 2015

IEEE Trans. on Image Process.

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“…In [15], abandoned object detection in video was studied. In [16], a method for detecting primary objects was proposed.…”

Section: Related Workmentioning

confidence: 99%

Personal object discovery in first-person videos

Liao

Jia

2015

IEEE Trans. on Image Process.

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People know and care for personal objects, which can be different for individuals. Automatically discovering personal objects is thus of great practical importance. We, in this paper, pursue this task with wearable cameras based on the common sense that personal objects generally company us in various scenes. With this clue, we exploit a new object-scene distribution for robust detection. Two technical challenges involved in estimating this distribution, i.e., scene extraction and unsupervised object discovery, are tackled. For scene extraction, we learn the latent representation instead of simply selecting a few frames from the videos. In object discovery, we build an interaction model to select frame-level objects and use nonparametric Bayesian clustering. Experiments verify the usefulness of our approach.

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“…On the other hand, while parametric active contour models are more sensitive to noise, parameter and contour initialization, and have issues with detecting high curvatures, their convergence is much faster and they can handle natural images with varying intra-object textures. Thus, the application of parametric models for automatic natural image segmentation, including salient object extraction [12], [13], [14], [15], [16] is well justified. Addressing the difficulties of the traditional active contour model [1], a group of parametric approaches were intended to redefine and improve the external energy of the contour [2], [3], [4], [5], [12], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Direction Selective Contour Detection for Salient Objects

Manno-Kovacs

2019

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-The active contour model is a widely used technique for automatic object contour extraction. Existing methods based on this model can perform with high accuracy even in case of complex contours, but challenging issues remain, like the need for precise contour initialization for high curvature boundary segments or the handling of cluttered backgrounds. To deal with such issues, this paper presents a salient object extraction method, the first step of which is the introduction of an improved edge map that incorporates edge direction as a feature. The direction information in the small neighborhoods of image feature points are extracted, and the images' prominent orientations are defined for direction-selective edge extraction. Using such improved edge information, we provide a highly accurate shape contour representation, which we also combine with texture features. The principle of the paper is to interpret an object as the fusion of its components: its extracted contour and its inner texture. Our goal in fusing textural and structural information is twofold: it is applied for automatic contour initialization, and it is also used to establish an improved external force field. This fusion then produces highly accurate salient object extractions. We performed extensive evaluations which confirm that the presented object extraction method outperforms parametric active contour models and achieves higher efficiency than the majority of the evaluated automatic saliency methods.

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Discovering Primary Objects in Videos by Saliency Fusion and Iterative Appearance Estimation

Cited by 34 publications

References 54 publications

Topical video object discovery from key frames by modeling word co-occurrence prior

Topical video object discovery from key frames by modeling word co-occurrence prior

Personal object discovery in first-person videos

Direction Selective Contour Detection for Salient Objects

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