IMAGE PROCESSING TEAM
[Abstracts of published articles]

[1]

Norbert Strobel, Chung-Sheng Li, and Vittorio Castelli, "Texture-Based Image Segmentation and MMAP for Digital Libraries'', submitted to the 1997 IEEE International Conference on Image Processing, Santa Barbara

Image segmentation is essential to obtain higher level object descriptions that are of significant importance in image database applications: methods that yield abstract image and region representations are, for instance, necessary to support query-by-example.ingest In this paper we address two aspects of the image segmentation problem with special focus on repositories of satellite images. First, we propose a texture-based image segmentation algorithm based on a small but descriptive set of features. Compact feature sets are needed for subsequent indexing methods necessary to organize image data. Then we introduce a modified maximum a posteriori (MMAP) post-processing technique. The MMAP is a detection method which combines local and global information to verify and correct initial segmentation results.

[2]

Norbert Strobel, Sanjit K. Mitra, and B.S. Manjunath, "Model-Based Detection and Correction of Corrupted Wavelet Coefficients'', submitted to the 1997 IEEE International Conference on Image Processing, Santa Barbara

Image decomposition based on the discrete wavelet transform (DWT) has been proposed for efficient storage and progressive transmission of images for visual browsing in digital image libraries. Although the compression aspects of the DWT have been carefully researched, reconstruction errors due to corrupted wavelet coefficients have received less attention. In this paper we consider the effect of a noisy channel on uniformly quantized wavelet coefficients and propose an error concealment method which, based on a local image model, simultaneously detects and corrects the noise-like error effects.

[3]

W. Y. Ma and B. S. Manjunath Edge Flow: A Framework of Boundary Detection and Image Segmentation ECE Technical Report #97-02, University of California, Santa Barbara

A novel boundary detection scheme based on "edge flow" is proposed in this paper. This scheme utilizes a predictive coding model to identify the direction of change in color and texture at each image location at a given scale, and constructs an edge flow vector. By iteratively propagating the edge flow, the boundaries can be detected at image locations which encounter two opposite directions of flow in the stable state. A user defined image scale is the only significant control parameter that is needed by the algorithm. The scheme also facilitates integration of color, texture, and phase into a single framework for boundary detection. Experimental results on a diverse collection of over 2,500 images from a stock photo library have yielded consistently good (visually acceptable) segmentation results.

[4]

W. Y. Ma and B. S. Manjunath, "Tools for texture/color based search of images," To appear in SPIE Int. Conf. 3106, Human Vision and Electronic Imaging II, Feb. 1997.

Currently there are quite a few image retrieval systems that use color and texture as features to search images. However, by using global features these methods retrieve results that often do not make much perceptual sense. It is necessary to constrain the feature extraction within homogeneous regions, so that the relevant information within these regions can be well represented. This paper describes our recent work on developing an image segmentation algorithm which is useful for processing large and diverse collections of image data. A compact color feature representation which is more appropriate for these segmented regions is also proposed. By using the color and texture features and a region-based search, we achieve a very good retrieval performance compared to the entire image based search.

[5]

[MAJASIS-97]. W. Y. Ma and B. S. Manjunath, "A Texture Thesaurus for Browsing Large Aerial Photographs," (under revision for publication in the Journal of the American Society for Information Science, 1997, special issue on AI)

A texture based image retrieval system for browsing large-scale aerial photographs is presented. The salient components of this system include texture feature extraction, image segmentation and grouping, learning similarity measure, and a texture thesaurus model for fast search and indexing. The texture features are computed by filtering the image with a bank of Gabor filters. This is followed by a texture gradient computation to segment each large airphoto into homogeneous regions. A hybrid neural network algorithm is used to learn the visual similarity by clustering patterns in the feature space. With learning similarity, the retrieval performance improves significantly. Finally, a texture image thesaurus is created by combining the learning similarity algorithm and a hierarchical vector quantization scheme. This thesaurus facilitates the indexing process while maintaining a good retrieval performance. Experimental results demonstrate the robustness of the overall system in searching over a large collection of aerial photographs.

[6]

B. S. Manjunath and W. Y. Ma, "Browsing large satellite and aerial photographs," IEEE Int. Conf. on Image Processing (invited paper), Vol. 2, pp. 765-768, Lausanne, Switzerland, Sep. 1996.

Image content based retrieval in the Alexandria digital library project has focussed on texture and color features for querying the database. A robust texture feature extraction algorithm and a fast segmentation scheme have been developed. The texture features are computed by filtering the image with a bank of Gabor filters. This is followed by a clustering scheme to create a texture based feature dictionary, which is then used to search and retrieve similar looking patterns from other images. Experimental results demonstrate the robustness of the overall system in searching over a large collection of airphotos and in selecting a surprisingly diverse collection of geographic features such as housing developments, parking lots, highways, and airports.

[7]

B. S. Manjunath and W.Y. Ma, "Texture Features for Browsing and Retrieval of Image Data," IEEE T-PAMI special issue on Digital Libraries, Vol. 18, No. 8, pp. 837-842, Aug. 1996.

Image content based retrieval is emerging as an important research area with application to digital libraries and multimedia databases. The focus of this paper is on the image processing aspects and in particular using texture information for browsing and retrieval of large image data. We propose the use of Gabor wavelet features for texture analysis and provide a comprehensive experimental evaluation. Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy. An application to browsing large air photos is illustrated.

[8]

W. Y. Ma and B. S. Manjunath, "Texture Features and Learning Similarity," Proceedings of IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 425-430, San Francisco, CA, June 1996.

This paper addresses two important issues related to texture pattern retrieval: feature extraction and similarity search. A Gabor feature representation for textured images is proposed, and its performance in pattern retrieval is evaluated on a large texture image database. These features compare favorably with other existing texture representations. A simple hybrid neural network algorithm is used to learn the similarity by simple clustering in the texture feature space. With learning similarity, the performance of similar pattern retrieval improves significantly. An important aspect of this work is its application to real image data. Texture feature extraction with similarity learning is used to search through large aerial photographs. Feature clustering enables efficient search of the database as our experimental results indicate.

[9]

Chandrasekaran, B.S. Manjunath, Y.F. Wang, J. Winkeler, and H. Zhang, "An Eigenspace Update Algorithm for Image Analysis," CS TR#96-04, April 1996. accepted for publication in the journal CVGIP: Graphical models and image processing.

During the past few years several interesting applications of eigenspace representation of the images have been proposed. These include face recognition, video coding, and pose estimation. However, the vision research community has largely overlooked parallel developments in signal processing and numerical linear algebra concerning efficient eigenspace updating algorithms. These new developments are significant for two reasons: Adopting them will make some of the current vision algorithms more robust and efficient. More important is the fact that incremental updating of eigenspace representations will open up new and interesting research applications in vision such as active recognition and learning. The main objective of this paper is to put these in perspective and discuss a new updating scheme for low numerical rank matrices that can be shown to be numerically stable and fast. A comparison with a non-adaptive SVD scheme shows that our algorithm achieves similar accuracy levels for image reconstruction and recognition at a significantly lower computational cost. We also illustrate applications to adaptive view selection for 3D object representation from projections.