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 introduces a new approach tocharacterize textures at multiple scales. The performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classi cation of twenty- ve natural textures. Both energy and entropy metrics were computed for each wavelet packet and incorporated into distinct scale space representations, where each wavelet packet (channel) re ected a speci c scale and orientation sensitivity. Wavelet packet representations for twenty- ve natural textures were classi ed without error by a simple two-layer network classi er. An analyzing function of large regularity (D 20) was shown to be slightly more e cient inrepresentation and discrimination than a similar function with fewer vanishing moments (D6). In addition, energy representations computed from the standard wavelet decomposition alone (17 features) provided classi cation without error for the twenty- ve textures included in our study. The reliability exhibited by texture signatures based on wavelet packets analysis suggest that the multiresolution properties of such transforms are bene cial for accomplishing segmentation, classication and subtle discrimination of texture. Index Terms{Feature extraction, texture analysis, texture classi cation, wavelet transform, wavelet packet, neural networks.

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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...

Abstract—Texture features that are based on the local power spectrum obtained by a bank of Gabor filters are compared. The features differ in the type of nonlinear post-processing which is applied to the local power spectrum. The following features are considered: Gabor energy, complex moments, and grating cell operator features. The capability of the corresponding operators to produce distinct feature vector clusters for different textures is compared using two methods: the Fisher criterion and the classification result comparison. Both methods give consistent results. The grating cell operator gives the best discrimination and segmentation results. The texture detection capabilities of the operators and their robustness to nontexture features are also compared. The grating cell operator is the only one that selectively responds only to texture and does not give false response to nontexture features such as object contours. Index Terms—Classification, complex moments, discrimination,

INTRODUCTION Pictorial pattern recognition systems are often described as consisting of three parts: a preprocessing part, a feature extraction part, and a classification part. The preprocessing is used to enhance or sharpen the image to be processed. This is usually done using linear operations or operations on the gray scale such as thresholding [1-3] The classification part is fairly well understood [4,5]. The feature extractor, on the other hand, is very much dependent upon the actual problem and no general theory has emerged on how to deal with it. Feature extraction procedures so far have been ad hoc, often referred to as "a bag of tricks." The present work grew out of an interest in finding a single picture operator that could in parallel perform a number of useful operations and that could work on several levels in a hierarchy. One background to this interest is the feeling that the eyes and brains of humans and animals are likely to have such standard operators, as the micro

Texture is an important part of the visual world of animals and humans and their visual systems successfully detect, discriminate, and segment texture. Relatively recently progress was made concerning structures in the brain that are presumably responsible for texture processing. Neurophysiologists reported on the discovery of a new type of orientation selective neuron in areas V1 and V2 of the visual cortex of monkeys which they called grating cells. Such cells respond vigorously to a grating of bars of appropriate orientation, position and periodicity. In contrast to other orientation selective cells, grating cells respond very weakly or not at all to single bars which do not make part of a grating. Elsewhere we proposed a nonlinear model of this type of cell and demonstrated the advantages of grating cells with respect to the separation of texture and form information. In this paper, we use grating cell operators to obtain features and compare these operators in texture analysis tas...

This article presents a mathematical denition of texture { the Julesz ensemble h), which is the set of all images (defined on Z²) that share identical statistics h. Then texture modeling is posed as an inverse problem: given a set of images sampled from an unknown Julesz ensemble h ), we search for the statistics h which define the ensemble. A Julesz ensemble h) has an associated probability distribution q(I; h), which is uniform over the images in the ensemble and has zero probability outside. In a companion paper [32], q(I; h) is shown to be the limit distribution of the FRAME (Filter, Random Field, And Minimax Entropy) model[35] as the image lattice ! Z². This conclusion establishes the intrinsic link between the scientific definition of texture on Z² and the mathematical models of texture on finite lattices. It brings two advantages to computer vision. 1). The engineering practice of synthesizing texture images by matching statistics has been put on a mathematical fou...

We propose a multi-level video event detection methodology and apply it to animal hunt detection in wildlife documentaries. The proposed multi-level approach has three levels. The first level extracts color, texture, and motion features, and detects moving object blobs. The mid-level employs a neural network to verify whether the moving object blobs belong to animals. This level also generates shot descriptors that combine features from the first level and contain results of mid-level, domain specific inferences made on the basis of shot features. The shot descriptors are then used by the domain-specific inference process at the third level to detect the video segments that contain hunts. The proposed approach can be applied to different domains by adapting the mid and high-level inference processes. Event based video indexing, summarization and browsing are among the applications of the proposed approach. Keywords Video content analysis; content-based indexing and retrieval; browsin...

Abstract—The histogram of image intensities is used extensively for recognition and for retrieval of images and video from visual databases. A single image histogram, however, suffers from the inability to encode spatial image variation. An obvious way to extend this feature is to compute the histograms of multiple resolutions of an image to form a multiresolution histogram. The multiresolution histogram shares many desirable properties with the plain histogram including that they are both fast to compute, space efficient, invariant to rigid motions, and robust to noise. In addition, the multiresolution histogram directly encodes spatial information. We describe a simple yet novel matching algorithm based on the multiresolution histogram that uses the differences between histograms of consecutive image resolutions. We evaluate it against five widely used image features. We show that with our simple feature we achieve or exceed the performance obtained with more complicated features. Further, we show our algorithm to be the most efficient and robust.