SIFT-Bag kernel for video event analysis

Zhou, Xi; Zhuang, Xinhua; Yan, Shuicheng; Chang, Shih-Fu; Hasegawa‐Johnson, Mark; Huang, Thomas S.

doi:10.1145/1459359.1459391

Cited by 94 publications

(75 citation statements)

References 26 publications

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“…Event detection using GMMs of scale-invariant feature transform (SIFT) features [19] from unconstrained news shots was proposed in [20]. To the best of our knowledge, we are the first to apply this framework to general event detection in consumer-generated videos.…”

Section: Figure 1 Examples Of Event Clips In Trecvid2010 and Trecvid2mentioning

confidence: 99%

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Event detection in consumer videos using GMM supervectors and SVMs

2013

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In large-scale multimedia event detection, complex target events are extracted from a large set of consumer-generated web videos taken in unconstrained environments. We devised a multimedia event detection method based on Gaussian mixture model (GMM) supervectors and support vector machines. A GMM supervector consists of the parameters of a GMM for the distribution of low-level features extracted from a video clip. A GMM is regarded as an extension of the bag-of-words framework to a probabilistic framework, and thus, it can be expected to be robust against the data insufficiency problem. We also propose a camera motion cancelled feature, which is a spatio-temporal feature robust against camera motions found in consumer-generated web videos. By combining these methods with the existing features, we aim to construct a high-performance event detection system. The effectiveness of our method is evaluated using TRECVID MED task benchmark.

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Section: Figure 1 Examples Of Event Clips In Trecvid2010 and Trecvid2mentioning

confidence: 99%

“…SIFT [19] has been effective in several image (e.g., [21]) and video application (e.g., [20]). SURF [22] needs several times less computation than SIFT.…”

Section: Figure 1 Examples Of Event Clips In Trecvid2010 and Trecvid2mentioning

confidence: 99%

Event detection in consumer videos using GMM supervectors and SVMs

2013

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“…The centroid-based classification method explored in [48] is also related to our method. It uses a NCM classifier and an 2 distance in a subspace that is orthogonal to the subspace with maximum within-class variance.…”

Section: Metric Learningmentioning

confidence: 99%

Large Scale Metric Learning for Distance-Based Image Classification on Open Ended Data Sets

Mensink¹,

Verbeek²,

Perronnin³

2013

Advanced Topics in Computer Vision

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Many real-life large-scale datasets are open-ended and dynamic: new images are continuously added to existing classes, new classes appear over time, and the semantics of existing classes might evolve too. Therefore, we study large-scale image classification methods that can incorporate new classes and training images continuously over time at negligible cost. To this end we consider two distancebased classifiers, the k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers. Since the performance of distance-based classifiers heavily depends on the used distance function, we cast the problem into one of learning a low-rank metric, which is shared across all classes. For the NCM classifier we introduce a new metric learning approach, and we also introduce an extension to allow for richer class representations.Experiments on the ImageNet 2010 challenge dataset, which contains over one million training images of thousand classes, show that, surprisingly, the NCM classifier compares favorably to the more flexible k-NN classifier. Moreover, the NCM performance is comparable to that of linear SVMs which obtain current state-of-theart performance. Experimentally we study the generalization performance to classes that were not used to learn the metrics. Using a metric learned on 1,000 classes, we show results for the ImageNet-10K dataset which contains 10,000 classes, and obtain performance that is competitive with the current state-of-the-art, while being orders of magnitude faster.

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“…Another similar part-based image represenations that are proposed recentlty are visterms [15,23,24], SIFT-bags [39] blobs [7], and VLAD [14] vector representation of an image which aggregates descriptors based on a locality criterion in the feature space. The different approach is the one proposed by Morand et al [21].…”

Section: Analogy Between Information Retrieval and Cbirmentioning

confidence: 99%

Toward a higher-level visual representation for content-based image retrieval

Sayad

Martinet

Urruty

et al. 2010

Multimed Tools Appl

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Having effective methods to access the desired images is essential nowadays with the availability of a huge amount of digital images. The proposed approach is based on an analogy between content-based image retrieval and text retrieval. The aim of the approach is to build a meaningful mid-level representation of images to be used later on for matching between a query image and other images in the desired database. The approach is based firstly on constructing different visual words using local patch extraction and fusion of descriptors. Secondly, we introduce a new method using multilayer pLSA to eliminate the noisiest words generated by the vocabulary building process. Thirdly, a new spatial weighting scheme is introduced that consists of weighting visual words according to the probability of each visual word to belong to each of the n Gaussian. Finally, we construct visual phrases from groups of visual words that are involved in strong association rules. Experimental results show that our approach outperforms the results of traditional image retrieval techniques.

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SIFT-Bag kernel for video event analysis

Cited by 94 publications

References 26 publications

Event detection in consumer videos using GMM supervectors and SVMs

Event detection in consumer videos using GMM supervectors and SVMs

Large Scale Metric Learning for Distance-Based Image Classification on Open Ended Data Sets

Toward a higher-level visual representation for content-based image retrieval

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