Design and evaluation of human-machine communication for image information mining

Daschiel, H.; Datcu, Mihai

doi:10.1109/tmm.2005.858383

Cited by 12 publications

(4 citation statements)

References 16 publications

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“…The data is quasi completely described in an unsupervised, application-free manner in the first three levels of the representation. Level 1 represents the spectral, geometrical and texture features extracted from the image data (level 0) using different stochastic signal models [2]. The features will then parameterize the input data with a certain amount of certainty (probability) corresponding to the model.…”

Section: Knowledge Centered Data Mining Systemmentioning

confidence: 99%

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User image mining for natural hazards

Popescu

Vaduva

Faur

et al. 2010

2010 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR)

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Natural hazards such as floods, avalanches, volcanic eruptions or earthquakes inflict losses to human settlements and manmade infrastructure and have negative impact on the local economy and environment. The growing number of satellite missions and space services facilitates the access to information for researches and decision makers. Space imagery offers the necessary support for risk prevention and mitigation, but requires human visual inspection which can be subjective, time consuming and ineffective. This paper aims to prove the efficiency of an intelligent knowledge driven EO information mining system in supporting natural hazards' management, by combining large database systems with visual capabilities. Two scenarios are envisaged both considering damage assessment for natural hazards like earthquakes and tsunami.

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Section: Knowledge Centered Data Mining Systemmentioning

confidence: 99%

“…The user's semantic interpretation of the image content (cover type L) is linked to the signal classes using simple Bayesian networks. The labels are trained in an interactive way by the user's positive and negative feedback samples using a graphical user interface [2]. …”

mentioning

confidence: 99%

User image mining for natural hazards

Popescu

Vaduva

Faur

et al. 2010

2010 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR)

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“…pixels, features or semantics [1,2,3]. Until now most research works focus on the features of image, e.g.…”

Section: Introductionmentioning

confidence: 99%

Contour Points based P2P Algorithm for Shape Matching and Image Retrieval

Zhao¹,

Ji²,

Sun³

et al. 2014

Appl. Math. Inf. Sci.

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Based on the extracted contours of objects from images, one point to point (P2P) method is proposed for shape matching and image retrieval. Taking contour of one object as reference, contour of another object is transformed to reach their best match, during which the similarity is evaluated by comparison of two corresponding sets of contour points. Translation, scaling and rotation are all considered in transformation, thus the algorithm is robust to objects with different position, size and posture. The experimental results are presented and compared with those from two popular shape based techniques, Hu invariant moments and Zernike moments. Performance of our new approach has proved its efficiency in both matching accuracy and computational expense, and it can be used in related applications together with the other kinds of shape features or even color features, texture features, etc.

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“…However, most of the existing high-level semantic feature mining methods are based on pixel-level features. Datcu et al [16] and Daschiel and Datcu [17] developed a Bayesian classifier to retrieve images from a remotely sensed image database by approximating the probabilities of images belonging to different classes using pixel-level probabilities. Aksoy et al [1] proposed a pixel-based Bayesian framework for a visual grammar to narrow the gap between low-level features and high-level concepts.…”

Section: Introductionmentioning

confidence: 99%

Remotely sensed image retrieval based on region-level semantic mining

Liu

Zhang

et al. 2012

J Image Video Proc

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As satellite images are widely used in a large number of applications in recent years, content-based image retrieval technique has become important tools for image exploration and information mining; however, their performances are limited by the semantic gap between low-level features and high-level concepts. To narrow this semantic gap, a region-level semantic mining approach is proposed in this article. Because it is easier for users to understand image content by region, images are segmented into several parts using an improved segmentation algorithm, each with homogeneous spectral and textural characteristics, and then a uniform region-based representation for each image is built. Once the probabilistic relationship among image, region, and hidden semantic is constructed, the Expectation Maximization method can be applied to mine the hidden semantic. We implement this approach on a dataset consisting of thousands of satellite images and obtain a high retrieval precision, as demonstrated through experiments.

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Design and evaluation of human-machine communication for image information mining

Cited by 12 publications

References 16 publications

User image mining for natural hazards

User image mining for natural hazards

Contour Points based P2P Algorithm for Shape Matching and Image Retrieval

Remotely sensed image retrieval based on region-level semantic mining

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