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Exploration Portal (Image exploration demo)

Exploration portal is a demonstration application for Multimedia exploration framework and a logical successor of SIR. The portal uses all framework features and implements example use cases for all important framework parts.

The portal can be used for exploration of static data sets, Bing search results and personal Facebook albums. It provides a variety of configuration options to affect feature extraction, similarity model, index creation and many other parameters.

The result is visualized using a similarity-based layout and supports different query options, such as zoom-in, zoom-out, multi-query or panning in 4 different directions.

Find the image (Online tool for comparisons of different multimedia exploration approaches)

Find the image is an artificial search scenario designed for testing and comparison of our exploration techniques. The task is to use a web-based exploration application to find as much images from a predetermined class as possible. This predetermined class should correspond to a search intention that cannot be easily transformed to a text-based query or to a query-by-example.

Multimedia exploration framework (Creation of efficient multimedia exploration applications)

Multimedia exploration framework is an extensible solution for creation of multimedia exploration applications.

It uses a modular architecture and already provides several implementantions for every component. Besides managing the software architecture and data flow, the framework also takes care of data source management, data retrieval, feature extraction, distance computation, metric indexing, query execution, data visiualization and GUI creation.

Contact the developers directly if you are interested in building an application using our framework.

SIR (Smart image retrieval)

When determining visual similarity of two images, it is evaluated on feature representations which consist of some content-based image properties. The conventional feature representations aggregate and store these properties in global feature histograms (e.g.,
MPEG-7 visual descriptors).

Recent feature representations, however, adaptively aggregate local image features in more flexible feature signatures, which can be
compared by adaptive similarity measures. The SIR engine developed at SIRET research group combines traditional MPEG-7 visual descriptors with feature signatures, leading to improved similarity search in image collections.

Currently, the SIR engine operates in a demo mode as a standalone image search engine. In order to manage large image collections in real time, the engine employs original database indexing technology. The SIR engine also includes meta-search functionality that allows to augment/rerank/explore results provided by other image search engines, such as Google Images and others. The actual version of the online re-ranking and exploration tool employes the particle physics model, that both distributes images on the screen and automatically creates visually similar clusters (as a side effect). To refer this tool, you can refer our publications - Image Exploration using Online Feature Extraction and Reranking (ICMR, 2012) and SIR: The Smart Image Retrieval Engine (SISAP, 2012).

Jakub Lokoc, Tomas Skopal