Color co-occurrence descriptors for querying-by-example

Kovalev, Vassili; Volmer, Stephan

doi:10.1109/mulmm.1998.722972

Cited by 59 publications

(29 citation statements)

References 12 publications

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“…Since the Brodatz dataset presents different categorization characteristics from MPEG-7 dataset, we changed same parameters of distance optimization algorithm [7]: we use top n = 30, th cohesion = 55, top nT oAdd = 8, and K = 15. Figure 9 presents the precision vs. recall curve for descriptors CCOM [19] and LAS [20] and the curve after the execution of the distance optimization algorithm based on distances correlation approach. We can observe that, for LAS descriptor, the distance optimization algorithm improved approximately 15% on end of curve, without any loss of precision.…”

Section: Experimental Results For Texture Descriptorsmentioning

confidence: 99%

Distances Correlation for Re-ranking in Content-Based Image Retrieval

Pedronette

Torres

2010

2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images

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Abstract-Content-based image retrieval relies on the use of efficient and effective image descriptors. One of the most important components of an image descriptor is concerned with the distance function used to measure how similar two images are. This paper presents a clustering approach based on distances correlation for computing the similarity among images. Conducted experiments involving shape, color, and texture descriptors demonstrate the effectiveness of our method.

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Section: Experimental Results For Texture Descriptorsmentioning

confidence: 99%

Distances Correlation for Re-ranking in Content-Based Image Retrieval

Pedronette

Torres

2010

2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images

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“…Due to the characteristic textural appearance, color texture descriptors are the most common type of features used in histology image analysis when describing the image as a whole. Among them are the color co-occurrence matrices introduced independently in [18] and [19] under the color correlogram term first [18] and as co-occurrence matrices a year later [19]. There are also several allied approaches for describing spatial image structure such as simultaneous autoregressive models [15] and some other.…”

Section: Methodsmentioning

confidence: 99%

A Method for Identification and Visualization of Histological Image Structures Relevant to the Cancer Patient Conditions

Kovalev

Dmitruk

Safonau

et al. 2011

Computer Analysis of Images and Patterns

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Abstract.A method is suggested for identification and visualization of histology image structures relevant to the key characteristics of the state of cancer patients. The method is based on a multi-step procedure which includes calculating image descriptors, extracting their principal components, correlating them to known object properties and mapping disclosed regularities all the way back up to the corresponding image structures they found to be linked with. Image descriptors employed are extended 4D color co-occurrence matrices counting the occurrence of all possible pixel triplets located at the vertices of equilateral triangles of different size. The method is demonstrated on a sample of 952 histology images taken from 68 women with clinically confirmed diagnosis of ovarian cancer. As a result, a number of associations between the patients' conditions and morphological image structures were found including both easily explainable and the ones whose biological substrate remains obscured.

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“…The texture experiments consider three well-known texture descriptors: Local Binary Patterns (LBP) [45], Color Co-Occurrence Matrix (CCOM) [46], and Local Activity Spectrum (LAS) [47]. We used the Brodatz dataset [48], which is composed of 111 different textures.…”

Section: Texture-based Experimentsmentioning

confidence: 99%

“…Each texture is divided into 16 blocks, such that 1776 images are considered. Figure 11 presents the P × t curves for the three descriptors and for the combination of LAS [47] + CCOM [46] descriptors. The results are consistent with shape and color descriptors, indicating the robustness of the proposed method for different visual properties.…”

Section: Texture-based Experimentsmentioning

confidence: 99%

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

Pedronette

Calumby

Torres

2015

J Image Video Proc.

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The interaction of users with search services has been recognized as an important mechanism for expressing and handling user information needs. One traditional approach for supporting such interactive search relies on exploiting relevance feedbacks (RF) in the searching process. For large-scale multimedia collections, however, the user efforts required in RF search sessions is considerable. In this paper, we address this issue by proposing a novel semisupervised approach for implementing RF-based search services. In our approach, supervised learning is performed taking advantage of relevance labels provided by users. Later, an unsupervised learning step is performed with the objective of extracting useful information from the intrinsic dataset structure. Furthermore, our hybrid learning approach considers feedbacks of different users, in collaborative image retrieval (CIR) scenarios. In these scenarios, the relationships among the feedbacks provided by different users are exploited, further reducing the collective efforts. Conducted experiments involving shape, color, and texture datasets demonstrate the effectiveness of the proposed approach. Similar results are also observed in experiments considering multimodal image retrieval tasks.

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Color co-occurrence descriptors for querying-by-example

Cited by 59 publications

References 12 publications

Distances Correlation for Re-ranking in Content-Based Image Retrieval

Distances Correlation for Re-ranking in Content-Based Image Retrieval

A Method for Identification and Visualization of Histological Image Structures Relevant to the Cancer Patient Conditions

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

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