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One line description: 
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).