Content-Based Image Retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many systems built. While these research efforts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Specifically, these efforts have relatively ignored two distinct characteristics of CBIR systems: (1) the gap between high level concepts and low level features; (2) subjectivity of human perception of visual content. This paper proposes a relevance feedback based interactive retrieval approach, which effectively takes into account the above two characteristics in CBIR. During the retrieval process, the user's high level query and perception subjectivity are captured by dynamically updated weights based on the user's feedback. The experimental results over more than 70,000 images show that the proposed approach greatly reduces the user's effort of composing a query and captures the user's i...

In the QBIC (Query By Image Content) project we are studying methods to query large on-line image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, and shape of image objects and regions. Potential applications include medical ("Give me other images that contain a tumor with a texture like this one"), photo-journalism ("Give me images that have blue at the top and red at the bottom"), and many others in art, fashion, cataloging, retailing, and industry. We describe a set of novel features and similarity measures allowing query by color, texture, and shape of image object. We demonstrate the effectiveness of the QBIC system with normalized precision and recall experiments on test databases containing over 1000 images and 1000 objects populated from commercially available photo clip art images, and of images of airplane silhouettes. We also consider the efficient indexing of these features, specifically addre...

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.

This paper provides a comprehensive survey of the technical achievements in the research area of Image Retrieval, especially Content-Based Image Retrieval, an area so active and prosperous in the past few years. The survey includes 100+ papers covering the research aspects of image feature representation and extraction, multi-dimensional 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. 1. INTRODUCTION Recent years have seen a rapid increase of the size of digital image collections. Everyday, both military and civilian equipment generates giga-bytes of images. Huge amount of information is out there. However, we can not access to or make use of the information unless it is organized so as to allow efficient browsing, searching and retriev...

Content-Based Image Retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many systems built. While these research efforts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Specifically, these e#orts have relatively ignored two distinct characteristics of CBIR systems: (1) the gap between high level concepts and low level features; (2) subjectivity of human perception of visual content. This paper proposes a relevance feedback based interactive retrieval approach, which effectively takes into account the above two characteristics in CBIR. During the retrieval process, the user's high level query and perception subjectivity are captured by dynamically updated weights based on the user's relevance feedback. The experimental results show that the proposed approach greatly reduces the user's effort of composing a query and captures the user's information need more precise...

Image texture is useful in image browsing, search and retrieval. A texture descriptor based on a multiresolution decomposition using Gabor wavelets is proposed. The descriptor consists of two parts: a perceptual browsing component (PBC) and a similarity retrieval component (SRC). The extraction methods of both PBC and SRC are based on a multiresolution decomposition using Gabor wavelets. PBC provides a quantitative characterization of the texture's structuredness and directionality for browsing application, and the SRC characterizes the distribution of texture energy in different subbands, and supports similarity retrieval. This representation is quite robust to illumination variations and compares favorably with other texture descriptors for similarity retrieval. Experimental results are provided.

Color is a feature of the great majority of content-based image retrieval systems. However the robustness, effectiveness, and efficiency of its use in image indexing are still open issues. This paper provides a comprehensive survey of the methods for color image indexing and retrieval described in the literature. In particular, image preprocessing, the features used to represent color information, and the measures adopted to compute the similarity between the features of two images are critically analyzed.

We determine the basic categories and the hierarchy of rules used by humans in judging similarity and matching of color patterns. The categories are 1) overall color; 2) directionality and orientation; 3) regularity and placement; 4) color purity; 5) complexity and heaviness.

Image texture is an important visual primitive in image search and retrieval applications. To characterize texture information of images, a texture descriptor is proposed to capture both the perceptual information and the statistics of textures. The descriptor consists of two parts: a Perceptual Browsing Component (PBC) and a Similarity Retrieval Component (SRC). The emphasis of this paper is to introduce the extraction method for the PBC which is based on multidimensional decomposition of images using Gabor filters. By analyzing the one dimensional projections resulting from filtered images, PCB gives a quantitative measure about the structuredness of textures and also identifies the directionality and coarseness (scale). Our experimental results demonstrate that PBC can capture these texture properties quite well.

With the expansion of the Internet, searching for information goes beyond the boundary of physical libraries. Millions of documents of various media types, suchas text, image, video, audio, graphics, and animation, are available around the world and linked by the Internet. Unfortunately, the state of the art of search engines for media types other than text lags far behind their text counterparts. To address this situation, wehave developed the Multimedia Analysis and Retrieval System #MARS#. This paper reports some of the progress made over the years towards exploring information retrieval beyond the text domain. In particular, the following aspects of MARS are addressed in the paper: visual feature extraction, retrieval models, query reformulation techniques, e#cient execution speed performance and user interface considerations. Extensive experimental results are reported to validate the proposed approaches. 1 Introduction Huge amounts of digital data are being generated...