Zhi-An Yi

Bio: Zhi-An Yi is an academic researcher. The author has contributed to research in topics: Image retrieval & Visual Word. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

A retrieval model in multiple level image information

Abstract: In this paper, the features of image are analyzed, and a multiple granularity hierarchy description model is proposed. Based on the model, a hierarchy image retrieval model is proposed.

Content-Based Image Retrieval by Feature Adaptation and Relevance Feedback

Abstract: The paper proposes an adaptive retrieval approach based on the concept of relevance-feedback, which establishes a link between high-level concepts and low-level features, using the user's feedback not only to assign proper weights to the features, but also to dynamically select them within a large collection of parameters. The target is to identify a set of relevant features according to a user query while at the same time maintaining a small sized feature vector to attain better matching and lower complexity. To this end, the image description is modified during each retrieval by removing the least significant features and better specifying the most significant ones. The feature adaptation is based on a hierarchical approach. The weights are then adjusted based on previously retrieved relevant and irrelevant images without further user-feedback. The algorithm is not fixed to a given feature set. It can be used with different hierarchical feature sets, provided that the hierarchical structure is defined a priori. Results achieved on different image databases and two completely different feature sets show that the proposed algorithm outperforms previously proposed methods. Further, it is experimentally demonstrated that it approaches the results obtained by state-of-the-art feature-selection techniques having complete knowledge of the data set.

A Flexible and Extensible Framework for Web Image Retrieval System

Abstract: Text-based image search engine and content-based image retrieval (CBIR) have achieved much progress in commercial and academic community respectively. However, few attempts have been conducted to integrate the two techniques for image retrieval in web context. In this paper, based on a novel web image data model, i.e. Fine-Grained Web Image Model (FGWIM), a flexible and extensible framework for web image retrieval is proposed, which incorporates highlevel semantics and low-level visual features of Web images and supports the visual part of MPEG-7 standard. FGWIM model describes the web image data in several levels of abstraction by fine-grained and structured representation, and gives multiple choices at each level, which provides a good flexibility and extensibility for further feature extraction, similarity measurement, integration of semantic and visual features. Based on FGWIM model and the framework, a web image retrieval system prototype is implemented.

MAIRS: a content-based multi-agent image retrieval system

Abstract: With the incessant expanding of information, the processing content of multimedia is becoming more and more extensive. To use the vast of information efficiently and effectively, a content-based retrieval system has been designed. The system is composed of the image gathering agent, the query submitting agent server, the color retrieval agent, the texture retrieval agent, and the shape retrieval agent and search results integration agent and result browser. The image gather agent is responsible for collecting images from network and storing them in image database. The query submitting agent server offers query samples to other agents and offers the cooperation between other agents. The color retrieval agent offers the retrieval ability based color features in the image database. The texture retrieval agent offers the retrieval ability based on texture features in the image database. The shape retrieval agent offers the retrieval ability based on shape features in the image database. Search results integration agent is responsible for integrating the color retrieval agent, the texture retrieval agent, the shape retrieval agent and the query submitting agent and browser, which obtains the retrieval request from the query submitting agent and browser, then sends them to each agent by means of primitive. At meantime, it combines the results returned by each agent and sends them to browser for the user browsing. The experimental results have showed that all agents in the system can work cooperatively to retrieve image information.