Dengsheng Zhang

Review of shape representation and description techniques

More and more images have been generated in digital form around the world. There is a growing interest in 1nding images in large collections or from remote databases. In order to 1nd an image, the image has to be described or represented by certain features. Shape is an important visual feature of an image. Searching for images using shape features has attracted much attention. There are many shape representation anddescription techniques in the literature. In this paper, we classify and review these important techniques. We examine implementation procedures for each technique and discuss its advantages and disadvantages. Some recent research results are also included and discussed in this paper. Finally, we identify some promising techniques for image retrieval according to standard principles.

Shape-based image retrieval using generic Fourier descriptor

Shape description is one of the key parts of image content description for image retrieval. Most of the existing shape descriptors are usually either application dependent or non-robust, making them undesirable for generic shape description. In this paper, a generic Fourier descriptor (GFD) is proposed to overcome the drawbacks of existing shape representation techniques. The proposed shape descriptor is derived by applying two-dimensional Fourier transform on a polar-raster sampled shape image. The acquired shape descriptor is application independent and robust. Experimental results show that the proposed GFD outperforms common contour-based and region-based shape descriptors.

A review on automatic image annotation techniques

Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task. Large amount of researches on image retrieval have been carried out in the past two decades. Traditionally, research in this area focuses on content based image retrieval. However, recent research shows that there is a semantic gap between content based image retrieval and image semantics understandable by humans. As a result, research in this area has shifted to bridge the semantic gap between low level image features and high level semantics. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) which extracts semantic features using machine learning techniques. In this paper, we focus on this latest development in image retrieval and provide a comprehensive survey on automatic image annotation. We analyse key aspects of the various AIA methods, including both feature extraction and semantic learning methods. Major methods are discussed and illustrated in details. We report our findings and provide future research directions in the AIA area in the conclusions

A Survey of Audio-Based Music Classification and Annotation

Music information retrieval (MIR) is an emerging research area that receives growing attention from both the research community and music industry. It addresses the problem of querying and retrieving certain types of music from large music data set. Classification is a fundamental problem in MIR. Many tasks in MIR can be naturally cast in a classification setting, such as genre classification, mood classification, artist recognition, instrument recognition, etc. Music annotation, a new research area in MIR that has attracted much attention in recent years, is also a classification problem in the general sense. Due to the importance of music classification in MIR research, rapid development of new methods, and lack of review papers on recent progress of the field, we provide a comprehensive review on audio-based classification in this paper and systematically summarize the state-of-the-art techniques for music classification. Specifically, we have stressed the difference in the features and the types of classifiers used for different classification tasks. This survey emphasizes on recent development of the techniques and discusses several open issues for future research.

Study and evaluation of different Fourier methods for image retrieval

Abstract Shape is one of the primary low-level image features exploited in the newly emerged content-based image retrieval (CBIR). Many shape methods exist. Among these shape methods, Fourier descriptor (FD) is one of the most widely used shape descriptors due to its simple computation, clarity and coarse to fine description capability. FD has been applied to a variety of applications, including image retrieval application. Generally, FD can be acquired in a number of ways, however, FD acquired from different ways can have different retrieval performance. In this paper, we study shape retrieval using FD. Specifically, we study different ways of acquiring FD, the number of FD features needed for general shape description and the retrieval performance of different FD. A Java client–server retrieval framework has been developed to facilitate the study. The retrieval performance of the different FD is tested using a standard shape database and a commonly used performance measurement.

A comparative study of curvature scale space and Fourier descriptors for shape-based image retrieval

Defective portions of a metallic tank storing hot hydrocarbon oil therein and having a thermally protective coating of polyurethane foam adhered onto the side wall thereof, are detected by determining a distribution of surface temperatures of the coating. Portions of the coating of relatively high temperatures correspond to defective portions of the coating which cause corrosion of the side wall of the tank, and in turn underlying corroded areas in the side wall.

Generic Fourier descriptor for shape-based image retrieval

Shape description is one of the key parts of image content description in MPEG-7. Most existing shape descriptors are usually either application dependent or non-robust, making them undesirable for generic shape description. A generic Fourier descriptor (GFD) is proposed to overcome the drawbacks of existing shape representation techniques. The proposed shape descriptor is derived by applying a 2D Fourier transform on a polar raster sampled shape image. The acquired shape descriptor is application independent and robust. Experimental results show the GFD outperforms the Zernike moment descriptor (ZMD) which has been proposed as the shape descriptor for MPEG-7.

Segmentation of moving objects in image sequence: A review

Segmentation of objects in image sequences is very important in many aspects of multimedia applications. In second-generation image/video coding, images are segmented into objects to achieve efficient compression by coding the contour and texture separately. As the purpose is to achieve high compression performance, the objects segmented may not be semantically meaningful to human observers. The more recent applications, such as content-based image/video retrieval and image/video composition, require that the segmented objects be semantically meaningful. Indeed, the recent multimedia standard MPEG-4 specifies that a video is composed of meaningful video objects. Although many segmentation techniques have been proposed in the literature, fully automatic segmentation tools for general applications are currently not achievable. This paper provides a review of this important and challenging area of segmentation of moving objects. We describe common approaches including temporal segmentation, spatial segmentation, and the combination of temporal-spatial segmentation. As an example, a complete segmentation scheme, which is an informative part of MPEG-4, is summarized.

Content-based shape retrieval using different shape descriptors: a comparative study

Shape representation is a fundamental issue in the newly emerging multimedia applications. In the content based image retrieval (CBIR), shape is an important low level image feature. Many shape representations have been proposed. However, for CBIR, a shape representation should satisfy several properties such as affine invariance, robustness, compactness, low computation complexity and perceptual similarity measurement. Against these properties, in this paper we attempt to study and compare several shape descriptors which have been widely adopted for CBIR, they are: Fourier descriptors (FD), curvature scale space (CSS) descriptors (CSSD), Zernike moment descriptors (ZMD) and grid descriptors (GD). The strengths and limitations of these methods are analyzed and clarified. Retrieval results are given to show the comparison.

Evaluation of MPEG-7 shape descriptors against other shape descriptors

Abstract.Shape is an important image feature - it is one of the primary low level image features exploited in content-based image retrieval (CBIR). There are generally two types of shape descriptors in the literature: contour-based and region-based. In MPEG-7, the curvature scale space descriptor (CSSD) and Zernike moment descriptor (ZMD) have been adopted as the contour-based shape descriptor and region-based shape descriptor, respectively. In this paper, the two shape descriptors are evaluated against other shape descriptors, and the two shape descriptors are also evaluated against each other. Standard methodology is used in the evaluation. Specifically, we use standard databases, large data sets and query sets, commonly used performance measurement and guided principles. A Java-based client-server retrieval framework has been implemented to facilitate the evaluation. Results show that Fourier descriptor (FD) outperforms CSSD, and that CSSD can be replaced by either FD or ZMD.