Refereed full papers (journals, book chapters, international conferences)

2001

@inbook{ZhG2001,
	vgclass =	{refpap},
	vgproject =	{cbir},
	author =	{Rong Zhao and William I. Grosky},
	title =	{Bridging the Semantic Gap in Image Retrieval},
	editor =	{T. K. Shih},
	booktitle =	{Distributed Multimedia Databases: Techniques and
	Applications},
	address =	{Hershey, Pennsylvania, USA},
	chapter =	{II},
	pages =	{14--36},
	publisher =	{Idea Group Publishing},
	year =	{2001},
	url =	{http://www.cs.sunysb.edu/\~{}rzhao/publications/SemanticGap.pdf},
	abstract =	{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, and
	spatial relationships, or semantic primitives.
	
	Conventional information retrieval was based solely on text, and those
	approaches to textual information retrieval have been transplanted into
	image retrieval in a variety of ways. However,  a picture is worth a
	thousand words . Image contents are much more versatile compared with
	texts, and the amount of visual data is already enormous and still
	expanding very rapidly.  Hoping to cope with these special
	characteristics of visual data, content-based image retrieval methods
	have been introduced. It has been widely recognized that the family of
	image retrieval techniques should become an integration of both
	low-level visual features addressing the more detailed perceptual
	aspects and high-level semantic features underlying the more general
	conceptual aspects of visual data. Neither of these two types of
	features is sufficient to retrieve or manage visual data in an
	effective or efficient way. Although efforts have been devoted to
	combining these two aspects of visual data, the gap between them is
	still a huge barrier in front of researchers. Intuitive and heuristic
	approaches do not provide us with satisfactory performance. Therefore,
	there is an urgent need of finding the latent correlation between
	low-level features and high-level concepts and merging them from a
	different perspective. How to find this new perspective and bridge the
	gap between visual features and semantic features has been a major
	challenge in this research field. Our paper addresses these issues.},
}