MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

Characterizing growth types and analyzing growth density distribution in response to urban growth patterns in peri-urban areas of Lianyungang City

This paper presents an image destriping system for correcting striping noise of remote-sensing images. The developed system identifies stripe positions based on edge-detection and line-tracing algorithms. Pixels not affected by striping are used as control points to construct cubic spline functions describing spatial gray level distributions of an image. Detected stripes are corrected by replacing the pixels with more reasonable gray values computed from constructed spline functions. Gray values of clean pixels not affected by stripes are not altered to preserve data genuineness. Several experimental results demonstrate that the developed system can correctly detect stripes in remote-sensing images and effectively repair them. Evaluations of the results based on an quantitative image quality index indicate that the image quality has been improved significantly after destriping. The destriped images are not only visually more plausible but also can provide better interpretability and are more suitable for computerized analysis.

Striping Noise Detection and Correction of Remote Sensing Images

The cubic-spline interpolation (CSI) scheme can be utilized to obtain a better quality reconstructed image. It is based on the least-squares method with cubic convolution interpolation (CCI) function. Within the parametric CSI scheme, it is difficult to determine the optimal parameter for various target images. In this paper, a novel method involving the concept of opportunity costs is proposed to identify the most suitable parameter for the CCI function needed in the CSI scheme. It is shown that such an optimal four-point CCI function in conjunction with the least-squares method can achieve a better performance with the same arithmetic operations in comparison with the existing CSI algorithm. In addition, experimental results show that the optimal six-point CSI scheme together with cross-zonal filter is superior in performance to the optimal four-point CSI scheme without increasing the computational complexity.

Novel Approaches to the Parametric Cubic-Spline Interpolation

Image Compression is a demanding field in this era of communication. There is a need to study and analyze the literature for image compression, as the demand for images, video sequences and computer animation has increased at very high rate so that the increment is drastically over the years. Multimedia data whether graphics, audio, video data which is uncompress requires considerable transmission bandwidth and storage capacity. So this leads to the need of compression of images and all multimedia applications to save storage and transmission time. In this study we discuss different compression algorithms used to reduce size of images without quality reduction.

Image Compression: A Survey

A new cubic convolution spline interpolation (CCSI )for both one-dimensional (1-D) and two-dimensional (2-D) signals is developed in order to subsample signal and image compression data. The CCSI yields a very accurate algorithm for smoothing. It is also shown that this new and fast smoothing filter for CCSI can be used with the JPEG standard to design an improved JPEG encoder-decoder for a high compression ratio.

W.S. Hsieh

Papers

Image data compression using cubic convolution spline interpolation