Large-scale Satellite Image Browsing using Automatic Semantic Categorization

Parulekar, Ashish; Datta, Ritendra; Li, Jia; Wang, J.Z.

doi:10.1109/iccv.2005.257

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…Therefore the system incorporates an automated region identification algorithm. There are other existing techniques in the literature for content-based retrieval such as satellite image browsing using automatic semantic categorization [19], partial relevance in interactive facial image retrieval [20] and region-based image clustering and retrieval using multiple instance learning [21]. A detailed survey published in 2005 on content-based image retrieval can be found in [22].…”

Section: Review Of Cbir Systemsmentioning

confidence: 99%

MapReduce neural network framework for efficient content based image retrieval from large datasets in the cloud

Venkatraman

Kulkarni

2012

2012 12th International Conference on Hybrid Intelligent Systems (HIS)

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Recently, content based image retrieval (CBIR) has gained active research focus due to wide applications such as crime prevention, medicine, historical research and digital libraries. With digital explosion, image collections in databases in distributed locations over the Internet pose a challenge to retrieve images that are relevant to user queries efficiently and accurately. It becomes increasingly important to develop new CBIR techniques that are effective and scalable for real-time processing of very large image collections. To address this, the paper proposes a novel MapReduce neural network framework for CBIR from large data collection in a cloud environment. We adopt natural language queries that use a fuzzy approach to classify the colour images based on their content and apply Map and Reduce functions that can operate in cloud clusters for arriving at accurate results in real-time. Preliminary experimental results for classifying and retrieving images from large data sets were quite convincing to carry out further experimental evaluations.

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Section: Review Of Cbir Systemsmentioning

confidence: 99%

MapReduce neural network framework for efficient content based image retrieval from large datasets in the cloud

Venkatraman

Kulkarni

2012

2012 12th International Conference on Hybrid Intelligent Systems (HIS)

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“…in [1] the authors use Gabor descriptors and self-organizing maps for classification of aerial images in order to enable efficient content-based retrieval from the database of aerial images. Parulekar et al [2] classify satellite images into four semantic categories in order to enable fast and accurate browsing of the image database.…”

Section: Introductionmentioning

confidence: 99%

Aerial image classification using structural texture similarity

Risojević

Babić

2011

2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

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There is an increasing need for algorithms for automatic analysis of remote sensing images and in this paper we address the problem of semantic classification of aerial images. For the task at hand we propose and evaluate local structural texture descriptor and similarity measure. Nearest neighbor classifier based on the proposed descriptor and similarity measure, as well as image-toclass similarity, improves classification rates over the state-of-the-art on two datasets of aerial images. We evaluate the design choices and show that rich subband statistics, perceptually-based structural texture similarity measure and image-to-class similarity all contribute to the good performance of our classifier.

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Integrated Search Based on Image Contents

Sansó

Llull

Mesquida

et al.

IFIP International Federation for Information Processing

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This works describes a new metaphor to make a contents search in an image database and to recover the results obtained using an automatically selected bandwidth. The increasing use of electronic commerce and the consequent publication of image catalogues through internet make evident the necessity to incorporate alternative mechanisms to the traditional search by keywords. Our work explores the scope of image-content access of databases. We use textual description, shapes, patterns and color similarities. At a later stage and due to the cost of the transmission of high quality images, we analyze the available bandwidth and we adapt the size of the resulting images to cope with the user requirements.

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Large-scale Satellite Image Browsing using Automatic Semantic Categorization

Cited by 6 publications

References 16 publications

MapReduce neural network framework for efficient content based image retrieval from large datasets in the cloud

MapReduce neural network framework for efficient content based image retrieval from large datasets in the cloud

Aerial image classification using structural texture similarity

Integrated Search Based on Image Contents

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