Evaluation of retrieval performance is a crucial problem in content-based image retrieval (CBIR). Many di erent methods for measuring the performance of a system have been created and used by researchers. This article discusses the advantages and shortcomings of the performance measures currently used. Problems such as de ning a common image database for performance comparisons and a means of g...

Content-based image retrieval (CBIR) has been an active research area for more than two decades. However, while almost all images are stored in compressed form, the vast majority of CBIR algorithms operate in the (uncompressed) pixel domain. This not only leads to a computational overhead for feature calculation, it can also be demonstrated that image compression has a negative effect on retrie...

Content-based image retrieval (CBIR) algorithms have been seen as a promising access method for digital photo collections, sooner or later replacing the traditional text-based methods. Unfortunately, we have very little evidence of the usefulness of these algorithms in real user needs and contexts. One problem is that appropriately designed test collections are not available even for the basic ...

ISBN 978-93-82338-22-2 | © 2012 Bonfring Abstract--This paper provides a mining approach to the research area of relevance feedback (RF) in contentbased image retrieval (CBIR). Relevance feedback is a powerful technique in CBIR systems, in order to improve the performance of CBIR effectively. The drawbacks in CBIR are the features of the query image and the semantic gap between low-level featur...

Deep learning-based multi-task learning (MTL) methods have recently attracted attention for content-based image retrieval (CBIR) applications in remote sensing (RS). For a given set of tasks (e.g., scene classification, semantic segmentation, and reconstruction), existing MTL employ joint optimization algorithm on the direct aggregation task-specific loss functions. Such an approach may provide...

Learning the similarity between remote sensing (RS) images forms foundation for content-based RS image retrieval (CBIR). Recently, deep metric learning approaches that map semantic of into an embedding (metric) space have been found very popular in RS. A common approach relies on selection triplets similar (positive) and dissimilar (negative) to a reference called anchor. Choosing is difficult ...

With advances in the computer technology and the World Wide Web there has been an explosion in the amount and complexity of multimedia data that are generated, stored, transmitted, analyzed, and accessed. In order to extract useful information from this huge amount of data, many content based image retrieval (CBIR) systems have been developed in the last decade. A typical CBIR system captures i...

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). We outline the new search engine which matches images using both local and global image features ...

From the computational point of view, Content-based Image 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 problem due to their excellent flexibility, scalab...