Content-based multimedia information retrieval is the hot point of researchers in many domains. But traditional feature vector based retrieval method can not provide retrieval on the semantic level. Integrated with our image retrieval system, we propose a new approach to generate semantic template automatically in the process of relevance feedback, and construct a network of semantic template w...

Semantic clustering is an important and challenge task for content-based image database management. This paper proposes a semantic clustering learning technique, which collects the relevance feedback image retrieval transaction and uses hypergraph to represent images correlation ship, then obtains the semantic clusters by hypergraph partitioning. Experiments show that it is efficient and simple.

ÐThe paper presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, t...

CBIR alone won’t give perfect retrieval results due to semantic gap. To overcome the problem of semantic gap in CBIR, more than one Semantic Content Based Image Retrieval techniques are required which is known as Hybrid Classification System. Hence the proposed approach uses multiple machine learning techniques with combination of different classifiers like supervised and unsupervised, soft cla...

The image retrieval performance based on single feature is limited. For different kinds of images, it can not a better retrieval result. This paper raises image retrieval method based on weighted multi feature. In each kind of images, each feature precision is the weight evidence. On this basis, we research the existing semantic retrieval technology. Choosing the SVM classification theory which...

Video retrieval technology has always been the important application domain of image engineering. Aiming at the problem that low-level features cannot describe highlevel semantic completely and accurately in the contentbased video retrieval technology, this paper introduces the methods of image understanding and proposes a video semantic analysis framework using scene analysis for recognizing s...

The main limitations of the existing high level image retrieval approaches concern the high dependance on an external reliable resource (domain ontologies, learning sets, etc. ) and a model for mapping semantic and visual information. In this paper, we propose an image retrieval system integrating semantic and visual features. The idea is to automatically build a modular ontology for semantic i...

Content-based visual information retrieval (CBVIR) as a new generation (with new concepts, techniques, mechanisms, etc.) of visual information retrieval has attracted many interests from the database community. The research starts by using a low-level feature from more than a dozen years ago. The current focus has shifted to capture high-level semantics of visual information. This chapter will ...

Despite progress in image retrieval by using low-level features, such as colors, textures and shapes, the performance is still unsatisfied as there are existing gaps between low-level features and high-level semantic concepts (semantic gaps). In this research, we propose a novel image retrieval system based on bag-of-features (BoF) model by integrating scale invariant feature transform (SIFT) a...

This paper demonstrates an approach to content based image retrieval founded on the semantically meaningful labelling of images by high level visual categories. The image labelling is achieved by means of a set of trained neural network classi-ers which map segmented image region descriptors onto semantic class membership terms. It is argued that the semantic terms give a good estimate of the s...