Perceptual Shape-Based Natural Image Representation and Retrieval

International Conference on Semantic Computing (ICSC 2007) ◽

10.1109/icosc.2007.4338402 ◽

2007 ◽

Author(s):

Xiaofen Zheng ◽

Scott A. Sherrill-Mix ◽

Qigang Gao

Keyword(s):

Image Representation ◽

Natural Image

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Image Compression Concepts Overview

Encyclopedia of Information Science and Technology, Second Edition ◽

10.4018/978-1-60566-026-4.ch284 ◽

2011 ◽

pp. 1805-1811

Author(s):

Alan Wee-Chung Liew ◽

Ngai-Fong Law

Keyword(s):

Image Compression ◽

Basic Principle ◽

Image Representation ◽

Digital Camera ◽

Image Features ◽

Natural Image ◽

Jpeg 2000 ◽

Line Drawings ◽

Core Technology ◽

Lower Entropy

Image compression aims to produce a new image representation that can be stored and transmitted efficiently. It is a core technology for multimedia processing and has played a key enabling role in many commercial products, such as digital camera and camcorders. It facilitates visual data transmission through the Internet, contributes to the advent of digital broadcast system, and makes possible the storage on VCD and DVD. Despite a continuing increase in capacity, efficient transmission and storage of images still present the utmost challenge in all these systems. Consequently, fast and efficient compression algorithms are in great demand. The basic principle for image compression is to remove any redundancy in image representation. For example, simple graphic images such as icons and line drawings can be represented more efficiently by considering differences among neighbor pixels, as the differences always have lower entropy value than the original images (Shannon, 1948). These kinds of techniques are often referred to as lossless compression. It tries to exploit statistical redundancy in an image so as to provide a concise representation in which the original image can be reconstructed perfectly. However, statistical compression techniques alone cannot provide high compression ratio. To improve image compressibility, lossy compression is often used so that visually important image features are preserved while some fine details are removed or not represented perfectly. This type of compression is often used for natural images where the loss of some details is generally unnoticeable to viewers. This articles deals with image compression. Specifi- cally, it is concern with compression of natural color images because they constitute the most important class of digital image. First, the basic principle and methodology of natural image compression is described. Then, several major natural image compression standards, namely JPEG, JPEG-LS, and JPEG 2000 are discussed.

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Natural Image Classification Based on Improved Support Vector Machine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.58-60.2387 ◽

2011 ◽

Vol 58-60 ◽

pp. 2387-2391

Author(s):

Ying Jian Qi ◽

Zhi Wei Ou ◽

Bin Zhang ◽

Ting Zhan Liu ◽

Ying Li

Keyword(s):

Support Vector Machine ◽

Image Classification ◽

Ant Colony Algorithm ◽

Image Representation ◽

Image Features ◽

Ant Colony ◽

Function Parameter ◽

Support Vector ◽

Natural Image ◽

Improved Support Vector Machine

Local image representation based natural image classification is an important task. SIFT descriptors and bag-of-visterm (BOV)method have achieved very good results. Many studies focused on improving the representation of the image, and then use the support vector machine to classify and identify the image category. However, due to support vector machine its own characteristics, it shows inflexible and slower convergence rate for large samples，with the selection of parameters influencing the results for the algorithm very much. Therefore, this paper will use the improved support vector machine algorithm be based on ant colony algorithm in classification step. The method adopt dense SIFT descriptors to describe image features and then use two levels BOV method to obtain the image representation. In recognition step, we use the support vector machine as a classifier but ant colony optimization method is used to selects kernel function parameter and soft margin constant C penalty parameter. Experiment results show that this solution determined the parameter automatically without trial and error and improved performance on natural image classification tasks.

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