We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.

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Image retrieval: Ideas, influences, and trends of the new age

The PASCAL Visual Object Classes Challenge

A survey of content-based image retrieval with high-level semantics

Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.

Multivariate Density Estimation

▶ Addresses the accessibility, usability, and, ultimately, acceptability of Information Society Technologies by anyone, anywhere, at anytime, and through any media and device. ▶ Focuses on theoretical, methodological, and empirical research, of both technological and non-technological nature. ▶ Features papers that report on theories, methods, tools, empirical results, reviews, case studies, and best-practice examples.

Universal access in the information society

Recently, with the explosive growth of the World Wide Web resources of digital color images, much research effort has been devoted to the development of efficient content based image retrieval systems. In this contribution we propose a color indexing method based on the Gaussian Mixture Model of a histogram. The model parameters serve as signatures enabling fast and efficient color image retrieval. We present the results of performed experiments which show that proposed approach is robust to color image distortions introduced by compression artifacts. Therefore, the proposed method is well suited for indexing and retrieval of Internet based collections of color images stored in lossy formats.

Gaussian Mixture Model Based Approach to Color Image Retrieval

In this paper we address the problem of the reduction of multiplicative noise in digital images. This kind of image distortion, also known as speckle noise, severely decreases the quality of medical ultrasound images and therefore their effective enhancement and restoration is of vital importance for proper visual inspection and quantitative measurements. The structure of the proposed Pixel-Patch Similarity Filter (PPSF) is a weighted average of pixels in a processing block and the weights are determined calculating the sum of squared differences between the mean of a patch and the intensities of pixels of the local window at the block center. The structure of the proposed design is similar to the bilateral and non-local means filters, however we neglect the topographic distance between pixels, which decreases substantially its computational complexity. The new technique was evaluated on standard gray scale test images contaminated with multiplicative noise modelled using Gaussian and uniform distribution. Its efficiency was also assessed utilizing a set of simulated ultrasonographic images distorted by means of the Field II simulation software and real ultrasound images of a finger joint. The comparison with the state-of-the-art techniques revealed very high efficiency of the proposed filtering framework, especially for strongly degraded images. Visually, the homogeneous areas are smoother, while image edges and small details are better preserved. The experiments have shown that satisfactory results were obtained with patches consisting of only 9 samples belonging to a relatively small processing block of 7x7 pixels, which ensures low computational complexity of the proposed denoising scheme and allows its application in real-time image processing scenarios.

Application of the local similarity filter for the suppression of multiplicative noise in medical ultrasound images

The rapid development of the Internet in the early 1990s caused an explosive growth of publicly accessible multimedia
resources It created new viewpoint on storage, distribution and processing of enormous collections of images Along
with the development of the World Wide Web there is much effort dedicated to create a content-based image retrieval
systems which are able to efficiently index, retrieve and manage large scale databases In this paper we propose a color
indexing method based on the Gaussian Mixture Model of color histograms The model parameters serve as signatures
enabling fast and efficient color image retrieval In this paper we show that the proposed approach is robust to color image
distortions introduced by lossy compression artifacts and therefore it is well suited for indexing and retrieval of Internet
based collections of color images stored in lossy compression formats

Parametric approach to the retrieval of lossy compressed color images

In this paper a new method for the reduction of multiplicative noise in digital images is described. The proposed algorithm is a modification of the Mean-Shift (MS) filter which is based on the concept of the Non-Local Means (NLM) denoising. The proposed algorithm does not focus on single pixels only, as in the case of the mean-shift technique, but also on their neighborhoods. The performance of the novel approach is experimentally verified and the obtained results prove that the new design is superior both to the MS and NLM techniques.

Non-local Mean-Shift filter for the reduction of multiplicative noise in digital images

In this paper, we provide a new noise reduction method for the enhancement of the images of gene chips We demonstrate that the new technique is capable of reducing outliers present in microarray images while preserving the spot edges This paper contains the comparisons of the proposed technique with the standard schemes used in color image filtering, as well as examples of their efficiency when these methods are applied to typical microarray images

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Bogdan Smolka

Papers

Gaussian Mixture Model Based Approach to Color Image Retrieval

Application of the local similarity filter for the suppression of multiplicative noise in medical ultrasound images

Parametric approach to the retrieval of lossy compressed color images

Non-local Mean-Shift filter for the reduction of multiplicative noise in digital images

Adaptive Noise Reduction in Microarray Images Based on the Center-Weighted Vector Medians