Molpher aims to be a scalable and interactive software tool to aid exploration of chemical space, the vast universe containing all possible compounds. Many areas of chemical biology, such as drug discovery, rely heavily on chemical libraries offering compounds usable in the industrial processes. Given a set of molecules with desired characteristics, Molpher explores their common neighbourhood based on structural similarity, as it represents promising part of the chemical space to find new additions into those libraries. In order to decrease the chance of missing interesting parts of the space, Molpher offers the human researcher to observe and interactively alter the exploration process. Generated subspace is expected to be further tested for synthesizability and biological activity by other software tools.

Among main features, Molpher offers optimized parallel exploration algorithm, compound logging, dimension-reduced visualization of chemical space and interactive widget-based GUI. Codebase is extensible in terms of additional morphing operators, chemical fingerprints, similarity measures and visualization strategies to allow further experiments.

SETTER web-server utilizes SETTER (SEcondary sTructure-based TERtiary Structure Similarity Algorithm) method for fast and accurate structural pairwise alignment. The server is capable of comparing a pair of RNA structures or using one strucutre as a query and search against a user-defined database of RNA structures. The efficiency of the algorithm is given by the decomposition of the RNA structure into the set of non-overlapping generalized secondary structure motifs (GSSUs). GSSU usually resembles a hairpin motif possibly containing bulges and/or internal loops in its stem part. A segmentation to GSSUs offers good scalability with respect to the structure size (SETTER scales linearly with the structure size) because the number of residues in GSSUs (SETTER scales quadratically with the GSSU size) generally does not increase with increased size of the RNA structure. The underlying SETTER algorithm is both accurate and very fast, and does not impose limits on the size of aligned RNA structures. SETTER is able to compare a pair of even the largest RNA structure in less than one minute.

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 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.

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).

Sketch-based Video Browser (or Video Hunter) is a new interactive video retrieval tool focusing on known-item search (KIS) tasks. In KIS tasks, users know about some video scene, know it is contained in a collection, but do not know where it is located. Therefore, users have to search/browse the collection with advanced techniques enabling query initialization, result visualization and browsing. Given such support, known-item search is not restricted just to ideal query formulation. The Video Hunter tool has participated in the Video Browser Showdown, winning the competition in 2014 and 2015. In both years 2016 and 2017, the Video Hunter tool achieved the third place.