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Electronic Letters on Computer Vision and Image Analysis 13(2):45-46, 2014 Towards an interactive index structuring system for content-based image retrieval in large image databases (2014)
| Content Provider | CiteSeerX |
|---|---|
| Researcher | Phuong, Lai Hien Boucher, Alain Ogier, Jean-Marc |
| Abstract | In recent years, the expansion of acquisition, storage and transmission techniques and the success of tablet computers facilitate the development of many large image databases as well as the interactions with the users. This thesis [1] deals with the problem of Content-Based Image Retrieval (CBIR) on these huge masses of data. Among three CBIR phases (feature extraction, feature space structuring and retrieval), we are particularly in-terested in the structuring phase (normally called indexing phase), which plays a very important role in finding information in large databases. This phase aims at organizing the visual feature descriptors of all images into an efficient data structure in order to facilitate, accelerate and improve further retrieval. Instead of traditional struc-turing methods, clustering methods which organize image descriptors into groups of similar objects (clusters), without any constraint on the cluster size, are studied. The aim is to obtain an indexed structure more adapted to the retrieval of high dimensional and unbalanced data. Clustering can be done without prior knowledge (unsupervised clustering) or with a limited amount of prior knowledge (semi-supervised clustering). Due to the “semantic gap ” between high-level semantic concepts expressed by the user via the query and the low-level features automatically extracted from the images, the clustering results and therefore the retrieval results are generally different from the wishes of the user. In this thesis, we proposed to involve the user in the |
| File Format | |
| Publisher Date | 2014-01-01 |
| Access Restriction | Open |
| Subject Keyword | Content-based Image Retrieval Large Image Database Computer Vision Interactive Index Electronic Letter Image Analysis Prior Knowledge Cluster Size Unsupervised Clustering Structuring Phase Feature Extraction Clustering Result Efficient Data Structure Semi-supervised Clustering High-level Semantic Concept High Dimensional Traditional Struc-turing Method Feature Space Structuring Image Descriptor Many Large Image Database Large Database Low-level Feature Similar Object Huge Mass Visual Feature Descriptor Tablet Computer Indexed Structure Transmission Technique Semantic Gap Limited Amount Cbir Phase Indexing Phase Unbalanced Data Retrieval Result |
| Content Type | Text |
| Resource Type | Thesis |
