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Mari Partio

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Kansas State University

Indian Institute Of Technology, Roorkee

Ataturk University

Anna University

Oak Ridge National Laboratory

University of Oslo

Amirali Baniasadi

University of Victoria BC

João António Madeiras Pereira

Instituto Superior Técnico

METU

deepa saibannavar

VTU Belgaum, Karnataka, INDIA

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Papers by Mari Partio

2004 International Conference on Image Processing (ICIP) TEXTURE SIMILAIUTY EVALUATlON USING ORDINAL CO-OCCURRENCE

Co-occurrence matrices have been succcssfully used in texture analysis. However, due to noise and... more Co-occurrence matrices have been succcssfully used in texture analysis. However, due to noise and monotonic shirts in gray levels, traditional co-occurrence analysis may lead to crroneous results. Using the order of the gray values instead of the gray values themselves is shown to improvc the relrieval accuracy. Ordinal measures havc been used for many image processing tasks in the literature. In this paper, we proposc a novel combination of ordinal measures and co-occurrence matrices using local pixel pair comparisons. Features constructcd in this paper reprcsent the occurrence frequency of certain ordinal rclationships at different distances and orientations. The proposcd method gives encouraging results whcn comparing its retrieval performance to that of the traditional gray level co-occurrence matrices. In local hinary pattern approach [6, IO] a local ncighhorhood is thresholdcd at the gray value orthe center pixel into a binary paltem. The final texture feature is the histogram...

Content-based image retrieval using shape and texture attributes

TAMPERE UNIVERSITY OF TECHNOLOGY Degree Program in Electrical Engineering Institute of Signal Pro... more TAMPERE UNIVERSITY OF TECHNOLOGY Degree Program in Electrical Engineering Institute of Signal Processing Partio, Mari: Content-based Image Retrieval using Shape and Texture Attributes Master of Science Thesis, p. 70 Examiners: Professor Moncef Gabbouj, Researcher Bogdan Cramariuc Funding: Center of Excellence, SPAG, Academy of Finland Department of Electrical Engineering November 2002 Due to rapid increase in volume of image and video collections, traditional methods of indexing and retrieval using only keywords have become outdated. Therefore, alternative methods to describe images using their visual content have been developed. To produce and test algorithms for content-based image and video retrieval, MUVIS (Multimedia Video Indexing and Retrieval System) was developed at TUT. The goal of MUVIS is a fast, real-time and reliable audio/video (AV) browsing and indexing application, which is also capable of extracting some key features (such as color, texture and shape) of the AV media. Most of the existing image retrieval systems perform reasonably when using color features. However, retrieval accuracy using shape or texture features does not produce as good results. Therefore, this thesis investigates different methods of representing shape and texture in content-based image retrieval. Later, when appropriate segmentation algorithms are available some of these methods could also be applied to video object retrieval. The thesis presents two contributions: one is shape-based and the second is texture-based retrieval method. The former contribution concerns shape analysis and retrieval. Shape attributes can be roughly divided into two main categories: boundary-based and regionbased. Since the human visual system itself focuses on edges and ignores uniform regions, this thesis concentrates on boundary-based representations. A novel boundary-based method using distance transformation and ordinal correlation is developed in this thesis. Simulation results show that the proposed technique produced encouraging results when using MPEG-7 shape test database. The second contribution of the thesis is a constrained application in which the database contains a set of rock images. In this application, we applied a technique based on GrayLevel Co-occurrence matrices (GLCM) and compared the results with a well-known method from the literature. It was found that GLCM outperforms Gabor Wavelet features when considering retrieval time and visual quality of the results.

ResearchArticle An Ordinal Co-occurrence Matrix Framework for Texture Retrieval

Texture retrieval using ordinal co-occurrence features

Due to variations in illumination conditions in texture evaluation applications, gray scale invar... more Due to variations in illumination conditions in texture evaluation applications, gray scale invariance is an important property in texture similarity evaluation. Using the order of the gray values instead of the gray values themselves is shown to improve the retrieval accuracy. Ordinal measures have been used for many image processing tasks in the literature. In this paper, we propose a novel combination of ordinal measures and cooccurrence matrices using local thresholding. Features constructed in this paper represent the occurrence frequency of certain ordinal relationships at different distances and orientations. The proposed method gives encouraging results when comparing its retrieval performance to that of the rotation invariant local binary pattern approach and traditional gray level cooccurrence matrices.

Texture similarity evaluation using ordinal co-occurrence

2004 International Conference on Image Processing, 2004. ICIP '04.

Co-occurrence matrices have been succcssfully used in texture analysis. However, due to noise and... more Co-occurrence matrices have been succcssfully used in texture analysis. However, due to noise and monotonic shirts in gray levels, traditional co-occurrence analysis may lead to crroneous results. Using the order of the gray values instead of the gray values themselves is shown to improvc the relrieval accuracy. Ordinal measures havc been used for many image processing tasks in the literature. In this paper, we proposc a novel combination of ordinal measures and co-occurrence matrices using local pixel pair comparisons. Features constructcd in this paper reprcsent the occurrence frequency of certain ordinal rclationships at different distances and orientations. The proposcd method gives encouraging results whcn comparing its retrieval performance to that of the traditional gray level co-occurrence matrices.

Shape Similarity Estimation using Ordinal

ABSTRACT

Block-based ordinal co-occurrence matrices for texture similarity evaluation

IEEE International Conference on Image Processing 2005, 2005

In this paper we introduce a block-based approach for ordinal co-occurrence matrices aimed at imp... more In this paper we introduce a block-based approach for ordinal co-occurrence matrices aimed at improving robustness of the basic ordinal co-occurrence. Earlier, we have introduced two approaches for building ordinal co-occurrence matrices. One considers only the center pixel of a moving window as a seed point, compares it to its anti-causal neighbors and saves the occurrences of ordinal relations between pixels in the form of cooccurrence matrices. However, in that approach problems occur especially when considering textures with slightly varying gray levels in relatively large areas. The other method improves the robustness of the earlier method by considering also other pixels than the center pixel in the thresholded window as seed points. Main drawback of that method is the increased computational complexity. In order to avoid that, a block-based approach for building the ordinal co-occurrence matrices is introduced in this paper. Retrieval accuracy of the proposed method has shown to produce better results than existing techniques.

<title>Evaluation of shape correspondence using ordinal measures</title>

Storage and Retrieval for Media Databases 2002, 2001

ABSTRACT In this paper we present a novel approach to shape similarity estimation based on ordina... more ABSTRACT In this paper we present a novel approach to shape similarity estimation based on ordinal correlation. The proposed method operates in three steps: object alignment, contour to multilevel image transformation and similarity evaluation. This approach is suitable for use in CBIR, shape classification and performance evaluation of segmentation algorithms. The proposed technique produced encouraging results when applied on the MPEG-7 test data.

An Ordinal Co-occurrence Matrix Framework for Texture Retrieval

EURASIP Journal on Image and Video Processing, 2007

We present a novel ordinal co-occurrence matrix framework for the purpose of content-based textur... more We present a novel ordinal co-occurrence matrix framework for the purpose of content-based texture retrieval. Several particularizations of the framework will be derived and tested for retrieval purposes. Features obtained using the framework represent the occurrence frequency of certain ordinal relationships at different distances and orientations. In the ordinal co-occurrence matrix framework, the actual pixel values do not affect the features, instead, the ordinal relationships between the pixels are taken into account. Therefore, the derived features are invariant to monotonic gray-level changes in the pixel values and can thus be applied to textures which may be obtained, for example, under different illumination conditions. Described ordinal co-occurrence matrix approaches are tested and compared against other well-known ordinal and nonordinal methods.

An Extended Framework Structure in MUVIS for Content-based Multimedia Indexing and Retrieval

Proc. 4th International Conference: Sciences of Electronic, Technologies of Information and Telecommunications (SETIT 2007), Tunisia, Mar 1, 2007

Abstract: In this paper, we propose an extended framework structure designed for MUVIS multimedia... more Abstract: In this paper, we propose an extended framework structure designed for MUVIS multimedia indexing and retrieval scheme in order to achieve the dynamic integration and run-time execution for the following operations within the context of multimedia indexing and retrieval: Visual and aural feature extraction, shot boundary detection and spatial segmentation. We introduce a general structure of the extended framework, and describe its well-defined interface, the general-purpose instructions and the conditions for integrating ...

Shape Similarity Estimation using Ordinal Measures

Proc. Int. Workshop on Coding Techniques for Very Low Bit-Rate Video, 2010

Abstract—In this paper we present a novel approach to shape similarity estimation based on ordina... more Abstract—In this paper we present a novel approach to shape similarity estimation based on ordinal correlation. The proposed method operates in three steps: object alignment, contour to multilevel image transformation and similarity evaluation. This approach is suitable for use in CBIR. The proposed technique produced encouraging results when applied on the MPEG-7 test data. Index Terms—Content, Retrieval, Indexing, Image, Contour, Boundary, Shape, Ordinal, Correlation, Similarity, Measure.

Applying texture and color features to natural image retrieval

2003 Finnish Signal Processing Symposium, 2003

This paper evaluates the performance of texture and color features applied to natural image retri... more This paper evaluates the performance of texture and color features applied to natural image retrieval. Natural images usually contain both texture and color, and therefore those features should also be considered together in a retrieval process. Combination of texture and color features is important, since it may result in closer connection to semantic. The idea in this study is to empirically evaluate the effect of texture and color features to the query. Retrieval capability of those features is first evaluated separately and finally the ...

Rock texture retrieval using gray level co-occurrence matrix

Proc. of 5th Nordic Signal Processing Symposium, Oct 4, 2002

Nowadays, as the computational power increases, the role of automatic visual inspection becomes m... more Nowadays, as the computational power increases, the role of automatic visual inspection becomes more important. Therefore, also visual quality control has gained in popularity. This paper presents an application of gray level co-occurrence matrix (GLCM) to texturebased similarity evaluation of rock images. Retrieval results were evaluated for two databases, one consisting of the whole images and the other with blocks obtained by splitting the original images. Retrieval results for both databases were obtained by ...

Ordinal-Measure Based Shape Correspondence

EURASIP Journal on Advances in Signal Processing, 2002

We present a novel approach to shape similarity estimation based on distance transformation and o... more We present a novel approach to shape similarity estimation based on distance transformation and ordinal correlation. The proposed method operates in three steps: object alignment, contour to multilevel image transformation, and similarity evaluation. This approach is suitable for use in shape classification, content-based image retrieval, and performance evaluation of segmentation algorithms. The two latter applications are addressed in this paper. Simulation results show that in both applications our proposed measure performs quite well in quantifying shape similarity. The scores obtained using this technique reflect well the correspondence between object contours as humans perceive it.

2004 International Conference on Image Processing (ICIP) TEXTURE SIMILAIUTY EVALUATlON USING ORDINAL CO-OCCURRENCE

Co-occurrence matrices have been succcssfully used in texture analysis. However, due to noise and... more Co-occurrence matrices have been succcssfully used in texture analysis. However, due to noise and monotonic shirts in gray levels, traditional co-occurrence analysis may lead to crroneous results. Using the order of the gray values instead of the gray values themselves is shown to improvc the relrieval accuracy. Ordinal measures havc been used for many image processing tasks in the literature. In this paper, we proposc a novel combination of ordinal measures and co-occurrence matrices using local pixel pair comparisons. Features constructcd in this paper reprcsent the occurrence frequency of certain ordinal rclationships at different distances and orientations. The proposcd method gives encouraging results whcn comparing its retrieval performance to that of the traditional gray level co-occurrence matrices. In local hinary pattern approach [6, IO] a local ncighhorhood is thresholdcd at the gray value orthe center pixel into a binary paltem. The final texture feature is the histogram...

Content-based image retrieval using shape and texture attributes

TAMPERE UNIVERSITY OF TECHNOLOGY Degree Program in Electrical Engineering Institute of Signal Pro... more TAMPERE UNIVERSITY OF TECHNOLOGY Degree Program in Electrical Engineering Institute of Signal Processing Partio, Mari: Content-based Image Retrieval using Shape and Texture Attributes Master of Science Thesis, p. 70 Examiners: Professor Moncef Gabbouj, Researcher Bogdan Cramariuc Funding: Center of Excellence, SPAG, Academy of Finland Department of Electrical Engineering November 2002 Due to rapid increase in volume of image and video collections, traditional methods of indexing and retrieval using only keywords have become outdated. Therefore, alternative methods to describe images using their visual content have been developed. To produce and test algorithms for content-based image and video retrieval, MUVIS (Multimedia Video Indexing and Retrieval System) was developed at TUT. The goal of MUVIS is a fast, real-time and reliable audio/video (AV) browsing and indexing application, which is also capable of extracting some key features (such as color, texture and shape) of the AV media. Most of the existing image retrieval systems perform reasonably when using color features. However, retrieval accuracy using shape or texture features does not produce as good results. Therefore, this thesis investigates different methods of representing shape and texture in content-based image retrieval. Later, when appropriate segmentation algorithms are available some of these methods could also be applied to video object retrieval. The thesis presents two contributions: one is shape-based and the second is texture-based retrieval method. The former contribution concerns shape analysis and retrieval. Shape attributes can be roughly divided into two main categories: boundary-based and regionbased. Since the human visual system itself focuses on edges and ignores uniform regions, this thesis concentrates on boundary-based representations. A novel boundary-based method using distance transformation and ordinal correlation is developed in this thesis. Simulation results show that the proposed technique produced encouraging results when using MPEG-7 shape test database. The second contribution of the thesis is a constrained application in which the database contains a set of rock images. In this application, we applied a technique based on GrayLevel Co-occurrence matrices (GLCM) and compared the results with a well-known method from the literature. It was found that GLCM outperforms Gabor Wavelet features when considering retrieval time and visual quality of the results.

ResearchArticle An Ordinal Co-occurrence Matrix Framework for Texture Retrieval

Texture retrieval using ordinal co-occurrence features

Due to variations in illumination conditions in texture evaluation applications, gray scale invar... more Due to variations in illumination conditions in texture evaluation applications, gray scale invariance is an important property in texture similarity evaluation. Using the order of the gray values instead of the gray values themselves is shown to improve the retrieval accuracy. Ordinal measures have been used for many image processing tasks in the literature. In this paper, we propose a novel combination of ordinal measures and cooccurrence matrices using local thresholding. Features constructed in this paper represent the occurrence frequency of certain ordinal relationships at different distances and orientations. The proposed method gives encouraging results when comparing its retrieval performance to that of the rotation invariant local binary pattern approach and traditional gray level cooccurrence matrices.

Texture similarity evaluation using ordinal co-occurrence

2004 International Conference on Image Processing, 2004. ICIP '04.

Co-occurrence matrices have been succcssfully used in texture analysis. However, due to noise and... more Co-occurrence matrices have been succcssfully used in texture analysis. However, due to noise and monotonic shirts in gray levels, traditional co-occurrence analysis may lead to crroneous results. Using the order of the gray values instead of the gray values themselves is shown to improvc the relrieval accuracy. Ordinal measures havc been used for many image processing tasks in the literature. In this paper, we proposc a novel combination of ordinal measures and co-occurrence matrices using local pixel pair comparisons. Features constructcd in this paper reprcsent the occurrence frequency of certain ordinal rclationships at different distances and orientations. The proposcd method gives encouraging results whcn comparing its retrieval performance to that of the traditional gray level co-occurrence matrices.

Shape Similarity Estimation using Ordinal

ABSTRACT

Block-based ordinal co-occurrence matrices for texture similarity evaluation

IEEE International Conference on Image Processing 2005, 2005

In this paper we introduce a block-based approach for ordinal co-occurrence matrices aimed at imp... more In this paper we introduce a block-based approach for ordinal co-occurrence matrices aimed at improving robustness of the basic ordinal co-occurrence. Earlier, we have introduced two approaches for building ordinal co-occurrence matrices. One considers only the center pixel of a moving window as a seed point, compares it to its anti-causal neighbors and saves the occurrences of ordinal relations between pixels in the form of cooccurrence matrices. However, in that approach problems occur especially when considering textures with slightly varying gray levels in relatively large areas. The other method improves the robustness of the earlier method by considering also other pixels than the center pixel in the thresholded window as seed points. Main drawback of that method is the increased computational complexity. In order to avoid that, a block-based approach for building the ordinal co-occurrence matrices is introduced in this paper. Retrieval accuracy of the proposed method has shown to produce better results than existing techniques.

<title>Evaluation of shape correspondence using ordinal measures</title>

Storage and Retrieval for Media Databases 2002, 2001

ABSTRACT In this paper we present a novel approach to shape similarity estimation based on ordina... more ABSTRACT In this paper we present a novel approach to shape similarity estimation based on ordinal correlation. The proposed method operates in three steps: object alignment, contour to multilevel image transformation and similarity evaluation. This approach is suitable for use in CBIR, shape classification and performance evaluation of segmentation algorithms. The proposed technique produced encouraging results when applied on the MPEG-7 test data.

An Ordinal Co-occurrence Matrix Framework for Texture Retrieval

EURASIP Journal on Image and Video Processing, 2007

We present a novel ordinal co-occurrence matrix framework for the purpose of content-based textur... more We present a novel ordinal co-occurrence matrix framework for the purpose of content-based texture retrieval. Several particularizations of the framework will be derived and tested for retrieval purposes. Features obtained using the framework represent the occurrence frequency of certain ordinal relationships at different distances and orientations. In the ordinal co-occurrence matrix framework, the actual pixel values do not affect the features, instead, the ordinal relationships between the pixels are taken into account. Therefore, the derived features are invariant to monotonic gray-level changes in the pixel values and can thus be applied to textures which may be obtained, for example, under different illumination conditions. Described ordinal co-occurrence matrix approaches are tested and compared against other well-known ordinal and nonordinal methods.

An Extended Framework Structure in MUVIS for Content-based Multimedia Indexing and Retrieval

Proc. 4th International Conference: Sciences of Electronic, Technologies of Information and Telecommunications (SETIT 2007), Tunisia, Mar 1, 2007

Abstract: In this paper, we propose an extended framework structure designed for MUVIS multimedia... more Abstract: In this paper, we propose an extended framework structure designed for MUVIS multimedia indexing and retrieval scheme in order to achieve the dynamic integration and run-time execution for the following operations within the context of multimedia indexing and retrieval: Visual and aural feature extraction, shot boundary detection and spatial segmentation. We introduce a general structure of the extended framework, and describe its well-defined interface, the general-purpose instructions and the conditions for integrating ...

Shape Similarity Estimation using Ordinal Measures

Proc. Int. Workshop on Coding Techniques for Very Low Bit-Rate Video, 2010

Abstract—In this paper we present a novel approach to shape similarity estimation based on ordina... more Abstract—In this paper we present a novel approach to shape similarity estimation based on ordinal correlation. The proposed method operates in three steps: object alignment, contour to multilevel image transformation and similarity evaluation. This approach is suitable for use in CBIR. The proposed technique produced encouraging results when applied on the MPEG-7 test data. Index Terms—Content, Retrieval, Indexing, Image, Contour, Boundary, Shape, Ordinal, Correlation, Similarity, Measure.

Applying texture and color features to natural image retrieval

2003 Finnish Signal Processing Symposium, 2003

This paper evaluates the performance of texture and color features applied to natural image retri... more This paper evaluates the performance of texture and color features applied to natural image retrieval. Natural images usually contain both texture and color, and therefore those features should also be considered together in a retrieval process. Combination of texture and color features is important, since it may result in closer connection to semantic. The idea in this study is to empirically evaluate the effect of texture and color features to the query. Retrieval capability of those features is first evaluated separately and finally the ...

Rock texture retrieval using gray level co-occurrence matrix

Proc. of 5th Nordic Signal Processing Symposium, Oct 4, 2002

Nowadays, as the computational power increases, the role of automatic visual inspection becomes m... more Nowadays, as the computational power increases, the role of automatic visual inspection becomes more important. Therefore, also visual quality control has gained in popularity. This paper presents an application of gray level co-occurrence matrix (GLCM) to texturebased similarity evaluation of rock images. Retrieval results were evaluated for two databases, one consisting of the whole images and the other with blocks obtained by splitting the original images. Retrieval results for both databases were obtained by ...

Ordinal-Measure Based Shape Correspondence

EURASIP Journal on Advances in Signal Processing, 2002

We present a novel approach to shape similarity estimation based on distance transformation and o... more We present a novel approach to shape similarity estimation based on distance transformation and ordinal correlation. The proposed method operates in three steps: object alignment, contour to multilevel image transformation, and similarity evaluation. This approach is suitable for use in shape classification, content-based image retrieval, and performance evaluation of segmentation algorithms. The two latter applications are addressed in this paper. Simulation results show that in both applications our proposed measure performs quite well in quantifying shape similarity. The scores obtained using this technique reflect well the correspondence between object contours as humans perceive it.

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