language: en

A Comparison of Content-Based Image/Video Retrieval Systems

Fatma Ozcan
December 15, 1997

Abstract Recently, content-based retrieval of images and video has become a hot research area. The reason for this is the need for effective and efficient techniques, that meet user requirements, to access large volumes of digital images and video data. In [GR 95], previous approaches to content-based retrieval is said to have taken two directions. In the first approach, image contents are modeled as a set of attributes extracted manually and managed within the framework of conventional database-management systems. The second approach depends on an integrated feature-extraction/object-recognition subsystem to overcome the limitations of attribute-based retrieval. However, it has been also stated that [GR 95] recent content-based image retrieval systems recognized the need for synergy between these two approaches. There has been numerous efforts to build content-based image retrieval systems. These include, Chabot [OS 95], SCORE [ATY 95, ATY 96], CONIVAS [AD 96], Photobook [PPS 93], CANDID [KCH 95], JACOB [ALDV 96, LA 96], VisualSEEk [SC 96] and QBIC [FSN+ 95]. In the first place, these systems differ in their querying capabilities. Images could be retrieved through the use of color, texture, sketch, shape, volume, spatial constraints, browsing, motion, text and domain concepts. Current approaches to content-based image retrieval also differ in terms of image features extracted, their level of abstraction and the degree of domain independence. In this paper, a comparative survey on these systems is conducted to figure out design tradeoffs and different approaches to content-based image retrieval. Furthermore, a taxonomy of content-based retrieval systems, as well as a generic architecture, is provided.

1 Introduction