Results 181 to 190 of about 4,123 (220)
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CBIR on Grids

2006
From the computational point of view, Content-based Im- age Retrieval systems are potentially expensive and have user response times growing with the ever-increasing sizes of the databases associated to them This paper presents a grid implementation of a Content-based Image Retrieval system that offers a good cost/performance ratio to solve this ...
Oscar David Robles   +3 more
openaire   +1 more source

Exploration and search-by-similarity in CBIR

16th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2003), 2004
We deal with content-based image indexing and category retrieval in general databases. When large categories are searched, systems must explore the database. We introduce an interactive retrieval strategy designed for database exploration. The technique is based on a compound query and a dedicated similarity function.
Matthieu Cord   +2 more
openaire   +1 more source

Banknote Recognition as a CBIR Problem

2015
Automatic banknote recognition is an important aid for visually impaired users, which may provide a complementary evidence to tactile perception. In this paper we propose a framework for banknote recognition based on a traditional Content-Based Image Retrieval pipeline: given a test image, we first extract SURF features, then adopt a Bag of Features ...
SOSA GARCIA, JOAN, ODONE, FRANCESCA
openaire   +2 more sources

Toward a Personalized CBIR System

2002
A personalized CBIR system based on a unified framework of fuzzy logic is proposed in this study. The user preference in image retrieval can be captured and stored in a personal profile. Thus, images that appeal to the user can be effectively retrieved.
Chih-Yi Chiu   +2 more
openaire   +1 more source

A novel relevance feedback method for CBIR

World Wide Web, 2018
In this paper, we address the challenge about insufficiency of training set and limited feedback information in each relevance feedback (RF) round during the process of content based image retrieval (CBIR). We propose a novel active learning scheme to utilize the labeled and unlabeled images to build the initial Support Vector Machine (SVM) classifier ...
Yunbo Rao   +4 more
openaire   +1 more source

CBIR Service for Object Identification

2015
This paper proposes an architecture for an exact object detection system. The implementation as well as the communication between individual system components is detailed in the paper. Well known methods for feature detection and extraction were used. Fast and precise method for feature comparison is presented.
Josef Hák   +3 more
openaire   +1 more source

The Many Facets of Progressive Retrieval for CBIR

2008
Recently, progressive retrieval has been advocated as an alternate solution to multidimensional indexes or approximate techniques, in order to accelerate similarity search of points in multidimensional spaces. The principle of progressive search is to offer a first subset of the answers to the user during retrieval.
Bouteldja, Nouha   +2 more
openaire   +2 more sources

Texture element feature characterizations for CBIR

Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05., 2005
Colour and texture are the most common features used in CBIR systems today. In this paper, we wish to investigate structural methods of texture analysis for CBIR in view of their closeness to human perception and description of texture. In structural analysis, local patterns are the key (as is the case with humans), and when used as features may be ...
K. Jalaja   +3 more
openaire   +1 more source

Query Techniques for CBIR

2015
One of the fundamental functionalities of a content-based image retrieval system (CBIR) is answering user queries. The survey of query types and examples of systems using these particular queries are presented here. For our CBIR, we prepared the dedicated GUI to construct user designed query (UDQ).
openaire   +1 more source

Towards Automatic Detection of CBIRs Configuration

2012
Many Content Based Image Retrieval system s (CBIRs) have been invented in the last decade. The general mechanism of the search process is very similar for each of these CBIRs, and the calculation of rankings is determined by the comparison of features (low-, mid-, high-level).
Christian Vilsmaier   +4 more
openaire   +1 more source

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