Cross-domain video concept detection using adaptive svms

Yang, Jun; Yan, Rong; Hauptmann, Alexander G.

doi:10.1145/1291233.1291276

Cited by 602 publications

(507 citation statements)

References 12 publications

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“…To put our method into perspective of domain adaptation and provided that the feature dimensionality is the same across modalities X and X ⋆ , we compare the proposed SVM MMD with the instance-transfer approach that shares the data samples between the two modalities, i.e. SVM trained on union of image and privileged data (SVM Combined); and the model-transfer method that relies on parameter transfer from privileged (source) to image (target) space, such as adaptive SVM [19,39] (SVM Adaptive). For a given solution of the source task, w source , and training data of the target task, SVM Adaptive solves the following optimization problem:…”

Section: Methodsmentioning

confidence: 99%

Learning from the Mistakes of Others: Matching Errors in Cross-Dataset Learning

Sharmanska

Quadrianto

2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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Section: Methodsmentioning

confidence: 99%

Learning from the Mistakes of Others: Matching Errors in Cross-Dataset Learning

Sharmanska

Quadrianto

2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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“…A third form of transfer learning is model adaptation, where auxiliary data is used to regularize the parameters of a target model, which can be either generative [60][61][62][63][64][65] or discriminative [66][67][68][69][70]. Although this is sometimes denoted domain adaptation, the latter usually refers to methods that regularize the target feature space, rather than the models themselves.…”

Section: Related Workmentioning

confidence: 99%

Cross-modal domain adaptation for text-based regularization of image semantics in image retrieval systems

Pereira

Vasconcelos

2014

Computer Vision and Image Understanding

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a b s t r a c tIn query-by-semantic-example image retrieval, images are ranked by similarity of semantic descriptors. These descriptors are obtained by classifying each image with respect to a pre-defined vocabulary of semantic concepts. In this work, we consider the problem of improving the accuracy of semantic descriptors through cross-modal regularization, based on auxiliary text. A cross-modal regularizer, composed of three steps, is proposed. Training images and text are first mapped to a common semantic space. A regularization operator is then learned for each concept in the semantic vocabulary. This is an operator which maps the semantic descriptors of images labeled with that concept to the descriptors of the associated texts. A convex formulation of the learning problem is introduced, enabling the efficient computation of concept-specific regularization operators. The third step is the selection of the most suitable operator for the image to regularize. This is implemented through a quantization of the semantic space, where a regularization operator is associated with each quantization cell. Overall, the proposed regularizer is a non-linear mapping, implemented as a piecewise linear transformation of the semantic image descriptors to regularize. This transformation is a form of cross-modal domain adaptation. It is shown to achieve better performance than recent proposals in the domain adaptation literature, while requiring much simpler optimization.

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“…Some of the works are either limited to some specific domains (e.g. movies [12,13], TV videos [14,15,16] etc.) or focus on certain predefined content such as human face [17,18] and human activities [19].…”

Section: Related Workmentioning

confidence: 99%

Tagging based Efficient Web Video Event Categorization

Raghavendra¹

2014

IJCATR

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Web video categorization is one of the emerging research fields in the computer vision domain due to its massive volume growth in the internet which demands to discover events. Due to insufficient, noisy information and large intra class disparity makes it more daunting task to recognize the events. Most of the recent works focus on constrained (fixed camera, known environment) videos with supervised labelling to categorize the web videos. In this paper, we propose the subject based Part-Of-Speech (POS) Tagging technique with the assist of Named Entity Recognition (NER) and WordNet is applied on YouTube video titles to discover the events based on the subject, not on the objects visualized in the videos. Unsupervised learning method is used on high level features (titles) because of incoming videos are not known and large intra-class variations. For the experiment, we have chosen topics from Google Zeitgeist and downloaded the related videos from YouTube. A novel conclusion is derived from the experimental result that use of low level features will lead to a poor classification in discovering intra class events based on the subject of the videos.

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Cross-domain video concept detection using adaptive svms

Cited by 602 publications

References 12 publications

Learning from the Mistakes of Others: Matching Errors in Cross-Dataset Learning

Learning from the Mistakes of Others: Matching Errors in Cross-Dataset Learning

Cross-modal domain adaptation for text-based regularization of image semantics in image retrieval systems

Tagging based Efficient Web Video Event Categorization

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