A New Content-Based Image Retrieval Approach Based on Pattern Orientation Histogram

Lakdashti, Abolfazl; Moin, Mohammad Shahram

doi:10.1007/978-3-540-71457-6_54

Cited by 11 publications

(2 citation statements)

References 11 publications

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“…Pattern orientation histogram (POH) method is a new nonedge-based image retrieval technique based on the pattern orientations in spatial domain [18]. POH represents distribution of five types of patterns from each image and produces 80 bins histogram.…”

Section: Low-level Featuresmentioning

confidence: 99%

Content-Based Image Retrieval Based on Relevance Feedback and Reinforcement Learning for Medical Images

Lakdashti¹,

Ajorloo

2011

ETRI J

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To enable a relevance feedback paradigm to evolve itself by users’ feedback, a reinforcement learning method is proposed. The feature space of the medical images is partitioned into positive and negative hypercubes by the system. Each hypercube constitutes an individual in a genetic algorithm infrastructure. The rules take recombination and mutation operators to make new rules for better exploring the feature space. The effectiveness of the rules is checked by a scoring method by which the ineffective rules will be omitted gradually and the effective ones survive. Our experiments on a set of 10,004 images from the IRMA database show that the proposed approach can better describe the semantic content of images for image retrieval with respect to other existing approaches in the literature.

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Section: Low-level Featuresmentioning

confidence: 99%

Content-Based Image Retrieval Based on Relevance Feedback and Reinforcement Learning for Medical Images

Lakdashti¹,

Ajorloo

2011

ETRI J

Self Cite

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“…For the modality classification, caption information can help if captions are well controlled like in the radiology domain but the more general biomedical literature makes it hard to find the modality information in the caption. Past work has shown that the image modality can be extracted from the image itself using visual features, only [10][11][12]. Therefore, in this paper, purely visual methods are used for the classification.…”

Section: Introductionmentioning

confidence: 99%

Bag–of–Colors for Biomedical Document Image Classification

Herrera

Markonis

Müller

2013

Medical Content-Based Retrieval for Clinical Decision Support

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The number of biomedical publications has increased noticeably in the last 30 years. Clinicians and medical researchers regularly have unmet information needs but require more time for searching than is usually available to find publications relevant to a clinical situation. The techniques described in this article are used to classify images from the biomedical open access literature into categories, which can potentially reduce the search time. Only the visual information of the images is used to classify images based on a benchmark database of ImageCLEF 2011 created for the task of image classification and image retrieval. We evaluate particularly the importance of color in addition to the frequently used texture and grey level features. Results show that bags-of-colors in combination with the Scale Invariant Feature Transform (SIFT) provide an image representation allowing to improve the classification quality. Accuracy improved from 69.75% of the best system in ImageCLEF 2011 using visual information, only, to 72.5% of the system described in this paper. The results highlight the importance of color for the classification of biomedical images.

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Modality Classification for Medical Images Using Sparse Coded Affine-Invariant Descriptors

Gál

Solt

Kerre

et al. 2013

Lecture Notes in Computer Science

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A New Content-Based Image Retrieval Approach Based on Pattern Orientation Histogram

Cited by 11 publications

References 11 publications

Content-Based Image Retrieval Based on Relevance Feedback and Reinforcement Learning for Medical Images

Content-Based Image Retrieval Based on Relevance Feedback and Reinforcement Learning for Medical Images

Bag–of–Colors for Biomedical Document Image Classification

Modality Classification for Medical Images Using Sparse Coded Affine-Invariant Descriptors

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