This paper provides a comprehensive survey of the technical achievements in the research area of image retrieval, especially content-based image retrieval, an area that has been so active and prosperous in the past few years. The survey includes 100+ papers covering the research aspects of image feature representation and extraction, multidimensional indexing, and system design, three of the fundamental bases of content-based image retrieval. Furthermore, based on the state-of-the-art technology available now and the demand from real-world applications, open research issues are identified and future promising research directions are suggested. C ○ 1999 Academic Press 1.

Technology advances in the areas of Image processing (IP) and Information Retrieval (IR) have evolved separately for a long time. However, successful content-based image retrieval systems require the integration of the two. There is an urgent need to develop integration mechanisms to link the image retrieval model to text retrieval model, such that the well established textretrieval techniques can be utilized. Approaches of converting image feature vectors (IP domain) to weighted-term vectors (IR domain) are proposed in this paper. Furthermore, the relevance feedback technique fromtheIRdomainisusedincontent-based image retrieval to demonstrate the e ectiveness of this conversion. Experimental results show that the image retrieval precision increases considerably by using the proposed integration approach. 1.

There are currently two dominant interface types for searching and browsing large image collections: keywordbased search, and searching by overall similarity to sample images. We present an alternative based on enabling users to navigate along conceptual dimensions that describe the images. The interface makes use of hierarchical faceted metadata and dynamically generated query previews. A usability study, in which 32 art history students explored a collection of 35,000 fine arts images, compares this approach to a standard image search interface. Despite the unfamiliarity and power of the interface (attributes that often lead to rejection of new search interfaces), the study results show that 90% of the participants preferred the metadata approach overall, 97% said that it helped them learn more about the collection, 75% found it more flexible, and 72% found it easier to use than a standard baseline system. These results indicate that a category-based approach is a successful way to provide access to image collections.

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While advances in technology allow us to generate, transmit, and store large amounts of digital images, video, and audio, research in indexing and retrieval of multimedia information is still at its infancy. To ad-

Abstract. We have carried out a detailed evaluation of the use of texture features in a query-by-example approach to image retrieval. We used 3 radically different texture feature types motivated by i) statistical, ii) psychological and iii) signal processing points of view. The features were evaluated and tested on retrieval tasks from the Corel and TRECVID2003 image collections. For the latter we also looked at the effects of combining texture features with a colour feature. 1

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neighbor queries

Unlike traditional database management systems, in multimedia databases that support content-based retrieval over multimedia objects, it is difficult for users to express their exact information need directly in the form of a precise query. A typical interface supported by content-based retrieval systems allows users to express their query in the form of examples of objects similar to the ones they wish to retrieve. Such a user interface, however, requires mechanisms to learn the query representation from the examples provided by the user. In our previous work, we proposed a query refinement mechanism in which a query representation is modified by adding new relevant examples based on user feedback. In this paper, we describe query processing mechanisms that can efficiently support query expansion using multidimensional index structures.

Applications like multimedia retrieval require e#cient support for similarity search on large data collections. Yet, nearest neighbor search is a di#cult problem in high dimensional spaces, rendering e#cient applications hard to realize: index structures degrade rapidly with increasing dimensionality, while sequential search is not an attractive solution for repositories with millions of objects. This paper approaches the problem from a di#erent angle. A solution is sought in an unconventional storage scheme, that opens up a new range of techniques for processing k-NN queries, especially suited for high dimensional spaces. The suggested (physical) database design accommodates well a novel variant of branch-and-bound search, that reduces the high dimensional space quickly to a small candidate set. The paper provides insight in applying this idea to k-NN search using two similarity metrics commonly encountered in image database applications, and discusses techniques for its implementation in relational database systems. The e#ectiveness of the proposed method is evaluated empirically on both real and synthetic data sets, reporting the significant improvements in response time yielded.