Image-text retrieval plays a central role in bridging vision and language, which aims to reduce the semantic discrepancy between images texts. Most of existing works rely on refined words objects representation through data-oriented method capture word-object cooccurrence. Such approaches are prone ignore asymmetric action relation texts, that is, text has explicit (i.e., verb phrase) while ima...

Text– and content-based retrievals are the most widely used approaches for medical image retrieval. They capture the similarity between images from different perspectives: text–based methods rely on manual textual annotations or captions associated with images; contentbased approaches are based on the visual content of the images themselves such as colors and textures. Text-based retrieval can ...

With the rapid development of multimedia and network technology, people can access a large number of multimedia information. For people who want to make full use of multimedia information resources, the primary question is how to query the multimedia information of interest. Text query can be applied to multimedia information retrieval, but it has inherent deficiencies. One hand, text annotatio...

Search engines such as Google and Yahoo allow internet users to search through huge collections of images. These existing search engines are all text-based, i.e. the searching is based on the comparison of keywords with textual annotations. Content-based image retrieval (CBIR) is a more natural approach in which the searching is based on the actual content of the images. However, CBIR systems a...

Given an imagebase with tagged images, four types of tasks can be executed, i.e., content-based image retrieval, image annotation, text-based image retrieval, and query expansion. For any of these tasks the similarity on the concerned type of objects is essential. In this paper, we propose a framework to tackle these four tasks from a unified view. The essence of the framework is to estimate si...

Due to the development and improvement in internet with high speed for the last few years andthe availability of a large digital image collection, efficient image retrieval systems are required. Retrievalcan be text-based and content-based. In content-based, which is also called Content-Based Image retrieval(CBIR), low level features of images are extracted such as color, texture and shape. The...

Content-based image retrieval has been keenly calculated in numerous fields. This provides more active management and retrieval of images than the keyword-based method. So the content based image retrieval has become one of the liveliest researches in the past few years. As earlier, we were using the text-based approach where it initiate very boring and hard task for solving the purpose of imag...

iSee© category: Research article " Image retrieval " by Rajya Lakshmi et al. Abstract: Two of the main components of the visual information are texture and color. In this paper, a content-based image retrieval system (CBIR), which computes texture and color similarity among images, is presented. CBIR is a set of techniques for retrieving semantically-relevant images from an image database based...

Image retrieval is a very imperative area of digital image processing. Images can be retrieving from a large database on the basis of text, color, structure or content. Content-based image retrieval uses the visual fillings of an image such as texture, color, shape, & spatial layout to represent and index the image. In typical CBIR systems, the visual content of the pictures in the database are...