A comparative study of texture measures with classification based on featured distributions

We investigate the properties of a metric between two distributions, the Earth Mover's Distance (EMD), for content-based image retrieval. The EMD is based on the minimal cost that must be paid to transform one distribution into the other, in a precise sense, and was first proposed for certain vision problems by Peleg, Werman, and Rom. For image retrieval, we combine this idea with a representation scheme for distributions that is based on vector quantization. This combination leads to an image comparison framework that often accounts for perceptual similarity better than other previously proposed methods. The EMD is based on a solution to the transportation problem from linear optimization, for which efficient algorithms are available, and also allows naturally for partial matching. It is more robust than histogram matching techniques, in that it can operate on variable-length representations of the distributions that avoid quantization and other binning problems typical of histograms. When used to compare distributions with the same overall mass, the EMD is a true metric. In this paper we focus on applications to color and texture, and we compare the retrieval performance of the EMD with that of other distances.

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The Earth Mover's Distance as a Metric for Image Retrieval

Unsupervised texture segmentation using Gabor filters

This chapter reviews and discusses various aspects of texture analysis. The concentration is o the various methods of extracting textural features from images. The geometric, random field, fractal, and signal processing models of texture are presented. The major classes of texture processing pro lems such as segmentation, classification, and shape from texture are discussed. The possible applic tion areas of texture such as automated inspection, document processing, and remote sensing a summarized. A bibliography is provided at the end for further reading.

Texture analysis

We present here SIMPLIcity (semantics-sensitive integrated matching for picture libraries), an image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction, and integrated region matching based upon image segmentation. An image is represented by a set of regions, roughly corresponding to objects, which are characterized by color, texture, shape, and location. The system classifies images into semantic categories. Potentially, the categorization enhances retrieval by permitting semantically-adaptive searching methods and narrowing down the searching range in a database. A measure for the overall similarity between images is developed using a region-matching scheme that integrates properties of all the regions in the images. The application of SIMPLIcity to several databases has demonstrated that our system performs significantly better and faster than existing ones. The system is fairly robust to image alterations.

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SIMPLIcity: semantics-sensitive integrated matching for picture libraries

Texture feature performance for image segmentation

Keywords: LTS1 Reference LTS-ARTICLE-1994-002 Record created on 2006-06-14, modified on 2016-08-08

N-folded symmetries by complex moments in Gabor space

Complex moments of the Gabor power spectrum yield estimates of the N-folded symmetry of the local image content at different frequency scales, that is, they allow to detect linear, rectangular, hexagonal/triangular, and so on, structures with very fine to very coarse resolutions. Results from experiments on the unsupervised segmentation of real textures indicate their importance for image processing applications. Real geometric moments computed in Gabor space also provide for very powerful texture features, but lack the clear geometrical interpretation of complex moments. >

N-folded symmetries by complex moments in Gabor space and their application to unsupervised texture segmentation

A range image segmentation contest was organized in conjunction with ICPR'2000. The goal is to continue the effort of experimentally evaluating range image segmentation algorithms initiated by Hoover et al. (1996) and Powell et al. (1998). This paper summarizes the results of the contest.

Some further results of experimental comparison of range image segmentation algorithms

The goal of the Portuguese project "SmartVision: active vision for the blind" is to develop a small, portable and cheap yet intelligent and reliable system for assisting the blind and visually impaired while navigating autonomously, both in- and outdoor. In this article we present an overview of the prototype, design issues, and its different modules which integrate GPS and Wi-Fi localisation with a GIS, passive RFID tags, and computer vision. The prototype addresses global navigation for going to some destiny, by following known landmarks stored in the GIS in combination with path optimisation, and local navigation with path and obstacle detection just beyond the reach of the white cane. The system does not replace the white cane but complements it, in order to alert the user to looming hazards. In addition, computer vision is used to identify objects on shelves, for example in a pantry or refrigerator. The user-friendly interface consists of a four-button hand-held box, a vibration actuator in the handle of the white cane, and speech synthesis. In the near future, passive RFID tags will be complemented by active tags for marking navigation landmarks, and speech recognition may complement or substitute the vibration actuator.

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J. M. H. du Buf

Papers

Texture feature performance for image segmentation

N-folded symmetries by complex moments in Gabor space

N-folded symmetries by complex moments in Gabor space and their application to unsupervised texture segmentation

Some further results of experimental comparison of range image segmentation algorithms

The SmartVision Navigation Prototype for Blind Users