Current hashing-based image retrieval methods mostly assume that the database of images is static. However, this assumption not true in cases where databases are constantly updated (e.g., on Internet) and there exists problem concept drift. The online (also known as incremental) hashing have been proposed recently for they considered drift problem. Moreover, update hash functions dynamically by...

The birth of the XML standard and the growing use of images in electronic documents raised an open issue in information retrieval: image retrieval in semi-structured documents. This article presents a method to evaluate a semantic representation of images using the text and the document structure. More precisely, we propose a measure that evaluates the participation of each element of the docum...

In this paper, a new method for heuristics and intermediate features based image retrieval is proposed. Heuristic features are identified and directly stored into the database and easily retrieved also. An algorithm is used to convert low level features hue, saturation and intensity in HSI space to semantic based color names. Images can be retrieved by these semantic color names. For semantics ...

In this paper, a multi-level image representation model is developed and used to mine semantic feature hidden in the original remote sensing image. This model is consisted of three levels : region level, region feature level and semantic level. The first two levels aim at represent image content by using region feature. Semantic level aims at extracting hidden semantic feature. At last, interes...

Semantic image retrieval from large amounts of egocentric visual data requires to leverage powerful techniques for filling in the semantic gap. This paper introduces LEMoRe, a Lifelog Engine for Moments Retrieval, developed in the context of the Lifelog Semantic Access Task (LSAT) of the the NTCIR-12 challenge and discusses its performance variation on different trials. LEMoRe integrates classi...

This system proposes an ontology based framework for the semantic search in the image annotation process. The main objective of this approach is to use ontology for the semantic search in the image retrieval process. The ontology based framework is developed to define the image space. This system proposes a construction of semantic based approach for image representation using SVM and decision ...

The emergence of multimedia technology and the rapidly expanding image and video collections on the internet have attracted significant research efforts in providing tools for effective retrieval and management of visual data. Image retrieval is based on the availability of a representation scheme of image content. Image content descriptors may be visual features such as color, texture, shape, ...

Introduction: Content Based Image Retrieval (CBIR) is a method of image searching and retrieval in a  database. In medical applications, CBIR is a tool used by physicians to compare the previous and current  medical images associated with patients pathological conditions. As the volume of pictorial information  stored in medical image databases is in progress, efficient image indexing and retri...

This paper addresses the challenge of automatic annotation of images for semantic image retrieval. In this research, we aim to identify visual features that are suitable for semantic annotation tasks. We propose an image classification system that combines MPEG-7 visual descriptors and support vector machines. The system is applied to annotate cityscape and landscape images. For this task, our ...