Image Collection Organization and Its Application to Indexing, Browsing, Summarization, and Semantic Retrieval

• Published in: IEEE transactions on multimedia Vol. 9; no. 4; pp. 893 - 900
• Main Authors: Kherfi, M.L., Ziou, D.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.2007; Institute of Electrical and Electronic Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: and archiving databases; Applied sciences; Artificial intelligence; Browsing; Collection; Computer science; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Content based retrieval; Content understanding and knowledge molding; digital libraries; Exact sciences and technology; Feedback; Image retrieval; Image storage; Indexing; Information retrieval; Information systems. Data bases; Memory organisation. Data processing; Navigation; Organizing; Pattern recognition. Digital image processing. Computational geometry; Probabilistic methods; Probability theory; query; Retrieval; retrieving; searching; Semantics; Software; Software libraries; Studies; and archiving databases; Database query; Man machine system; Image content; searching; Modeling; retrieving; Relevance feedback; User interface; Semantics; Database; Linguistics; Electronic library; Multimedia; Molding; Probabilistic approach; Image retrieval; Abstract; Image databank; query; Information retrieval; digital libraries; Information browsing; Content understanding and knowledge molding; Catalogs; Indexing
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2007.893349

In this paper, we present a new framework for organizing image collections into structures that can be used for indexing, browsing, retrieval and summarization. Instead of using tree-based techniques which are not suitable for images, we develop a new solution that is specifically designed for image collections. We consider both low-level image content and high-level semantics in an attempt to alleviate the semantic gap encountered by many systems. The fact that our model is based on a probabilistic framework makes it possible to combine it in a natural way with probabilistic techniques developed recently for image retrieval. The structure our model generates is applied for four purposes. The first is to provide retrieval module with an index, which allows it to improve retrieval time and accuracy, while the second is to provide users with a hierarchical browsing catalog that allows them to navigate the image collection by subject. This represents an additional step towards facilitating human-computer interaction in the context of image retrieval and navigation. The third aim is to provide users with a summarization of the general content of each class in the collection, and the fourth is a retrieval mechanism. Related issues such as relevance feedback and feature selection are also addressed. The experiments at the end of the paper show that the proposed framework yields some significant improvements