Annotating retrieval database with active learning

Zhang, C.; Chen, T.

doi:10.1109/icip.2003.1246750

Cited by 13 publications

(13 citation statements)

References 15 publications

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“…3). The images are classified by graduate stu-dents into 52 semantic categories (such as airplane, car, building; see [10]) based on their visual appearance; one image can belong to multiple categories. Based on this classification, we build a 1750 by 1750 similarity table S; S(i, j) = 1 if image I i and I j belong to a common category; S(i, j) = 0 otherwise.…”

Section: Methodsmentioning

confidence: 99%

Content-Free Image Retrieval using Bayesian Product Rule

Liu

Chen

2006

2006 IEEE International Conference on Multimedia and Expo

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Content-free image retrieval uses accumulated user feedback records to retrieve images without analyzing image pixels. We present a Bayesian-based algorithm to analyze user feedback and show that it outperforms a recent maximum entropy content-free algorithm, according to extensive experiments on trademark logo and 3D model datasets. The proposed algorithm also has the advantage of being applicable to both content-free and traditional content-based image retrieval, thus providing a common framework for these two paradigms.

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Section: Methodsmentioning

confidence: 99%

Content-Free Image Retrieval using Bayesian Product Rule

Liu

Chen

2006

2006 IEEE International Conference on Multimedia and Expo

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“…Wu et al define a representativeness measure for each sample according to its distance to nearby samples, and take it as a criterion of sample selection [18]. Zhang et al estimate data distribution p(x) by Kernel Density Estimation (KDE), and then take it into account in sample selection [13,20].…”

Section: Densitymentioning

confidence: 99%

“…Here we adopt a similar approach as that proposed in [20], by which p(x) is estimated by KDE as follows…”

Section: Densitymentioning

confidence: 99%

Multi-Concept Multi-Modality Active Learning for Interactive Video Annotation

Wang

Hua

Yan

et al. 2007

International Conference on Semantic Computing (ICSC 2007)

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Active learning methods have been widely applied to reduce human labeling effort in multimedia annotation tasks. However, in traditional methods multiple concepts are usually sequentially annotated, i.e., each concept is exhaustively annotated before proceeding to the next, without taking the learnabilities of different concepts into consideration. Furthermore, in most of these methods only a single modality is applied. This paper presents a novel multiconcept multi-modality active learning method which exchangeably annotates multiple concepts in the context of multi-modality. It iteratively selects a concept and a batch of unlabeled samples, and then these samples are annotated with the selected concept. After that, a graph-based semi-supervised learning is conducted on each modality for the selected concept. The proposed method takes into account both the learnabilities of different concepts and the potentials of different modalities. Experimental results on TRECVID 2005 benchmark have demonstrated its effectiveness and efficiency.

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“…Some works use a thesaurus for annotation [5,7] to overcome the ambiguity problem. Some others pre-confine a small number of probable keywords for the image database [3,6,8] to alleviate both two problems. However the effectiveness of these approaches is modest.…”

Section: Introductionmentioning

confidence: 99%

“…Hidden annotation (HA) is an effective way to bridge this feature-to-concept gap [2][3][4][5][6][7][8], whose aim is to form highlevel semantic attributes for images. Most previous HA systems map an image into many keywords which directly reflect its semantic meaning, and combine the keyword information with low-level features to help retrieval.…”

Section: Introductionmentioning

confidence: 99%