Search results for "Search engine indexing"
showing 10 items of 56 documents
Interactive Gradually Generating Relevance Query Refinement Under the Human-Mediated Scenario in Multilingual Settings
2016
As opposed to query modelling, relevance generating interactive query refinement (QR) is a technique aimed at exploiting syntax variations of gradually extended, being removed or replaced with some other keywords query, which depending on the factors like e.g. the information resource, the database structure, or the keyword alignment, facilitates significantly the searching process. Therefore our motivation is to explore the dynamism of the precision trend depended upon the factors analyzed. For a couple of language pairs which constitute multilingual settings, we develop a user-centred framework that imposes distributed search optimization. Our data set contains variety of query types subm…
Content Based Indexing of Image and Video Databases by Global and Shape Features
1996
Indexing and retrieval methods based on the image content are required to effectively use information from the large repositories of digital images and videos currently available. Both global (colour, texture, motion, etc.) and local (object shape, etc.) features are needed to perform a reliable content based retrieval. We present a method for automatic extraction of global image features, like colour and motion parameters, and their use for data restriction in video database querying. Further retrieval is therefore accomplished, in a restricted set of images, by shape feature (skeleton, local symmetry moments, correlation, etc.) local search. The proposed indexing methodology has been deve…
Unifying Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web
1999
A system is proposed that combines textual and visual statistics in a single index vector for content-based search of a WWW image database. Textual statistics are captured in vector form using latent semantic indexing based on text in the containing HTML document. Visual statistics are captured in vector form using color and orientation histograms. By using an integrated approach, it becomes possible to take advantage of possible statistical couplings between the content of the document (latent semantic content) and the contents of images (visual statistics). The combined approach allows improved performance in conducting content-based search. Search performance experiments are reported for…
Video Indexing Using MPEG Motion Compensation Vectors
2003
In the last years a lot of work has been done on color, textural, structural and semantic indexing of "content-based" video databases. Motion-based video indexing has been less explored, with approaches generally based on the analysis of optical flows. Compressed videos require the decompression of the sequences and the computation of optical flows, two steps computationally heavy. In this paper we propose some methods to index videos by motion features (mainly related to camera motion) and by motion-based spatial segmentation of frames, in a fully automatic way. Our idea is to use MPEG motion vectors as an alternative to optical flows. Their extraction is very simple and fast; it doesn't r…
Video indexing using optical flow field
2002
The increasing development of advanced multimedia applications requires new technologies for organizing and retrieving by content databases of digital video. Several content based features (color, texture, motion, etc.) are needed to perform a reliable content based retrieval. We present a method for automatic motion based video indexing and retrieval. A prototypal system has been developed to prove the validity of our approach. Our system automatically splits a video into a sequence of shots, extracts a few representative frames (said r-frames) from each shot and computes some motion based features related to the optical flow field. Motion based queries are then performed either in a quali…
Combining textual and visual cues for content-based image retrieval on the World Wide Web
2002
A system is proposed that combines textual and visual statistics in a single index vector for content-based search of a WWW image database. Textual statistics are captured in vector form using latent semantic indexing (LSI) based on text in the containing HTML document. Visual statistics are captured in vector form using color and orientation histograms. By using an integrated approach, it becomes possible to take advantage of possible statistical couplings between the content of the document (latent semantic content) and the contents of images (visual statistics). The combined approach allows improved performance in conducting content-based search. Search performance experiments are report…
Optimisation des requêtes de similarité dans les espaces métriques répondant aux besoins des usagers
2012
The complexity of data stored in large databases has increased at very fast paces. Hence, operations more elaborated than traditional queries are essential in order to extract all required information from the database. Therefore, the interest of the database community in similarity search has increased significantly. Two of the well-known types of similarity search are the Range (Rq) and the k-Nearest Neighbor (kNNq) queries, which, as any of the traditional ones, can be sped up by indexing structures of the Database Management System (DBMS). Another way of speeding up queries is to perform query optimization. In this process, metrics about data are collected and employed to adjust the par…
VIRES: A distributed open architecture for pictorial database
2006
In this paper we describe VIRES (Visual Information Retrieval Extendible System) an open distributed pictorial database for image retrieval. The retrieval methods, pictorial indexing and data are distributed over the network. VIRES has been designed as an open architecture. The system is based on the concept of distributed model via dictionary in order to reach a good versatility without changing the kernel of VIRES.
Textual data compression in computational biology: a synopsis.
2009
Abstract Motivation: Textual data compression, and the associated techniques coming from information theory, are often perceived as being of interest for data communication and storage. However, they are also deeply related to classification and data mining and analysis. In recent years, a substantial effort has been made for the application of textual data compression techniques to various computational biology tasks, ranging from storage and indexing of large datasets to comparison and reverse engineering of biological networks. Results: The main focus of this review is on a systematic presentation of the key areas of bioinformatics and computational biology where compression has been use…
Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing
2020
The Tsetlin Machine (TM) is a machine learning algorithm founded on the classical Tsetlin Automaton (TA) and game theory. It further leverages frequent pattern mining and resource allocation principles to extract common patterns in the data, rather than relying on minimizing output error, which is prone to overfitting. Unlike the intertwined nature of pattern representation in neural networks, a TM decomposes problems into self-contained patterns, represented as conjunctive clauses. The clause outputs, in turn, are combined into a classification decision through summation and thresholding, akin to a logistic regression function, however, with binary weights and a unit step output function. …