https://webuser.hs-furtwangen.de/~heindl/ebte-2014ws-BCM%2014_Munmun%20Singhania.pdf

A review on automatic image annotation techniques

Call for papers for Special Issue of ACM Transactions on Multimedia Computing, Communications and Applications on Interactive Digital Television

ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMCCAP)

Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image

Grayscale and color textures can have spectral informative content. This spectral information coexists with the grayscale or chromatic spatial pattern that characterizes the texture. This informative and nontextural spectral content can be a source of confusion for rigorous evaluations of the intrinsic textural performance of texture methods. In this paper, we used basic image processing tools to develop a new class of textures in which texture information is the only source of discrimination. Spectral information in this new class of textures contributes only to form texture. The textures are grouped into two databases. The first is the Normalized Brodatz Texture database (NBT) which is a collection of grayscale images. The second is the Multiband Texture (MBT) database which is a collection of color texture images. Thus, this new class of textures is ideal for rigorous comparisons between texture analysis methods based only on their intrinsic performance on texture characterization.

/pdf/new-brodatz-based-image-databases-for-grayscale-color-and-18nmpplqc7.pdf

New Brodatz-Based Image Databases for Grayscale Color and Multiband Texture Analysis

Technological advances in digital imaging, broadband networking, and data storage are motivating millions of people to communicate with one another and express themselves by sharing images, video, and other forms of media online. Acquiring, storing, and transmitting photos is now trivial, but to manipulate, index, sort, filter, summarize, or search through them is significantly harder. The Statistical Visual Computing Laboratory at the University of California, San Diego, has been considering the problem of content-based image retrieval for years. This effort explores many issues in image representation and intelligent system design, including the evaluation of image similarity, automatic annotation of images with descriptive captions, and the ability to understand user feedback during image search.

/pdf/from-pixels-to-semantic-spaces-advances-in-content-based-fl15ylzxov.pdf

From pixels to semantic spaces: Advances in content-based image retrieval - eScholarship

Feature extraction is a key issue in content-based image retrieval (CBIR). In the past, a number of texture features have been proposed in literature, including statistic methods and spectral methods. However, most of them are not able to accurately capture the edge information which is the most important texture feature in an image. Recent researches on multi-scale analysis, especially the curvelet research, provide good opportunity to extract more accurate texture feature for image retrieval. Curvelet was originally proposed for image denoising and has shown promising performance. In this paper, a new image feature based on curvelet transform has been proposed. We apply discrete curvelet transform on texture images and compute the low order statistics from the transformed images. Images are then represented using the extracted texture features. Retrieval results show, it significantly outperforms the widely used Gabor texture feature.

https://users.monash.edu.au/~dengs/resource/papers/mmsp08.pdf

Content based image retrieval using curvelet transform

There have been much interest and a large amount of research on content based image retrieval (CBIR) in recent years due to the ever increasing number of digital images. Texture features play a key role in CBIR. Many texture features exist in literature, however, most of them are neither rotation invariant nor robust to scale and other variations. Texture features based on Gabor filters have been shown with significant advantages over other methods, and they are adopted by MPEG-7 as one of the texture descriptors for image retrieval. In this paper, we propose a rotation invariant curvelet features for texture representation. With systematic analysis and rigorous experiments, we show that the proposed curvelet texture features significantly outperforms the widely used Gabor texture features. A novel region padding method is also proposed to apply curvelet transform to region based image retrieval. Retrieval results from standard image databases show that curvelet features are promising for both texture and region representation.

Rotation Invariant Curvelet Features for Region Based Image Retrieval

Texture feature plays a vital role in content based Image retrieval (CBIR). Wavelet texture feature modeled by generalized Gaussian density (GGD) [1] performs better than discrete wavelet texture feature. Curve let texture feature was proposed in [2]. In this paper, we compute a new texture feature by applying the generalized Gaussian density to the distribution of curve let coefficients which we call curve let GGD texture feature. The purpose of this paper is to investigate curve let GGD texture feature and compare its retrieval performance with that of curve let, wavelet and wavelet GGD texture features. Experimental results show that both curve let and curve let GGD features perform significantly better than wavelet and wavelet GGD texture features. Among the two types of curve let based features, curve let feature shows better performance in CBIR than curve let GGD texture feature. The findings are discussed in the paper.

Ishrat Jahan Sumana

Papers

Content based image retrieval using curvelet transform

Rotation Invariant Curvelet Features for Region Based Image Retrieval

Comparison of Curvelet and Wavelet Texture Features for Content Based Image Retrieval