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

In this paper a new approach to the problem of noise removal in color images is presented. The proposed filtering design is a modification of the bilateral denosing scheme, which takes into account the similarity between color pixels and their spatial distance. However, instead of direct calculation of the dissimilarity measure, the cost of a connection through a digital path joining the central pixel of the filtering window with the remaining pixels is determined. The filter output, like in the standard bilateral filter, is calculated as a weighted average of the pixels surrounding the center of the filtering window, and the weights are functions of the minimal connection costs. Experimental results prove that the introduced design yields significantly better results than the bilateral filter in the case of color images contaminated by strong mixed Gaussian and impulsive noise.

Improved bilateral filtering scheme for noise removal in color images

In this paper a novel method of noise reduction in color images is presented. The new technique is capable of attenuating both impulsive and Gaussian noise, while preserving and even enhancing the sharpness of the image edges. Extensive simulations reveal that the new method outperforms significantly the standard techniques widely used in multivariate signal processing. In this work we apply the new noise reduction method for the enhancement of the images of gene chips. We demonstrate that the new technique is capable of reducing various kinds of noise present in microarray images and that it enables efficient spot location and estimation of the gene expression level, due to the smoothing effect and preservation of the spot edges. This paper contains the comparison of the new technique of noise reduction with the standard procedures used for the processing of vector valued images, as well as examples of the efficiency of the new algorithm when applied to typical microarray images.

Enhancement of the DNA microarray chip images

In this contribution we analyze the properties of a novel class of noise attenuating and edge enhancing filters for color image processing. The new filtering design is a generalization of the well known Vector Median Filter. The new filter class is minimizing the cumulated dissimilarity measure of a cluster of pixels belonging to the sliding filtering window and outputs the pixel centrally located within the so called peer group of pixels. The described filter is computationally efficient, easy to implement and very effective in suppressing impulsive noise, while preserving image details and strongly enhancing its edges.

Generalized Vector Median Filter

In this paper a new technique of mixed Gaussian and impulsive noise suppression in color images is proposed. The novel approach is based on a weighted average of pixels contained in the filtering block. The main novelty of the proposed solution lies in the new definition of similarity between a pixel and samples belonging to a small window centered at the central pixel of the processing block. Instead of direct comparison of pixels, a measure based on the similarity between a given pixel and the samples from the window is utilized. This measure is defined as the sum of distances in a given color space between a pixel and a certain number of most similar pixels from this window. In this way, the new similarity measure is not influenced by the outliers injected into the image by the impulsive noise process and the averaging process ensures the effectiveness of the new filter in the reduction of Gaussian noise. The experimental results prove that the novel filtering design is capable of suppressing even strong mixed noise and is competitive with respect to state-of-the-art methods.

On the robust technique of mixed Gaussian and impulsive noise reduction in color digital images

This paper presents a new filtering scheme for the removal of impulsive noise in multichannel images. It is based on the estimation of the probability density function for pixels in a filtering window, by means of the kernel density estimation method. The filtering algorithm itself is based on the comparison of pixels with their neighborhood in a sliding window. The results obtained with the new filter show its excellent ability to reduce impulsive noise while simultaneously preserving fine image details.

Bogdan Smolka

Papers

Improved bilateral filtering scheme for noise removal in color images

Enhancement of the DNA microarray chip images

Generalized Vector Median Filter

On the robust technique of mixed Gaussian and impulsive noise reduction in color digital images

Kernel density estimation based multichannel impulsive noise reduction filter