Content-based image retrieval using PSO and k-means clustering algorithm

Younus, Zeyad Safaa; Mohamad, Dzulkifli; Saba, Tanzila; Alkawaz, Mohammed Hazim; Rehman, Amjad; Al-Rodhaan, Mznah; Al-Dhelaan, Abdullah

doi:10.1007/s12517-014-1584-7

Cited by 94 publications

(51 citation statements)

References 18 publications

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“…It is an extensively used visual feature in CBIR that moderates robust and simple processing. Color sensitivity and color space were, respectively, proposed in finding color similar to how humans perceive color (Zeyad et al 2014;Komali et al 2012). Texture features are also intended to capture the granularity and repetitive patterns of surfaces within an image.…”

Section: An Overviewmentioning

confidence: 99%

“…The gray-level histogram defined the mathematical equations given in Eqs. 1 and 2 (Zeyad et al 2014), where I (x, y) represents the probability of pixel falling into the bin:…”

Section: Color Featuresmentioning

confidence: 99%

“…However, they are not sufficient to describe the large variety of textures. According to Zeyad et al (2014), the co-occurrence matrix C (i, j) is used to compute the co-occurrence of pixels with gray values i and j as a given distance that is the polar coordinates (d,) with discrete length and orientation. In practice, h takes the values 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°, respectively.…”

Section: Texture Featuresmentioning

confidence: 99%

“…Moreover, keyword-based image retrieval is not appropriate, because there is no fixed set of words that describes image content. The contentbased image retrieval (CBIR) was presented to address the difficulties related to the text-based image retrieval (Zeyad et al 2014). The CBIR system practises image content to search and retrieve digital images.…”

Section: Introductionmentioning

confidence: 99%

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A medical image retrieval scheme through a medical social network

Ayadi¹,

Bouslimi²,

Akaichi³

2016

Netw Model Anal Health Inform Bioinforma

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Medical social networking sites have enabled multimedia content sharing in large volumes by allowing physicians and patients to upload their medical images. Moreover, it is necessary to employ new techniques to effectively handle and benefit from them. This huge volume of images needs to formulate new types of queries that pose complex questions to medical social network databases. Content-based image retrieval (CBIR) stills an active and efficient research topic to manipulate medical images. To palliate this situation, we propose in this paper the integration of a content-based medical image retrieval method through a medical social network, based on an efficient fusion of low-level visual image features (color, shape and texture features) which offers an efficient and flexible precision. A clear application of our CBIR system consists of providing stored images that are visually similar to a new (undiagnosed) one, allowing specialist and patients to check past exam diagnoses from comments and other physicians' annotations, and to establish, therefore, a new diagnostic or to prepare a new report of an image's exam. Experiments show that the proposed medical image retrieval scheme achieves better performance and accuracy in retrieving images. However, we need also to verify whether our approach is considered by the specialists as a potential aid in a real environment. To do so, we evaluate our methodology's impact in the user's decision, inquiring the specialists about the degree of confidence in our system. By analyzing the obtained results, we can argue that the proposed methodology presented a high acceptance regarding the specialists' interests in the clinical practice domain and can improve the decision-making process during analysis.

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Section: An Overviewmentioning

confidence: 99%

“…The gray-level histogram defined the mathematical equations given in Eqs. 1 and 2 (Zeyad et al 2014), where I (x, y) represents the probability of pixel falling into the bin:…”

Section: Color Featuresmentioning

confidence: 99%

Section: Texture Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A medical image retrieval scheme through a medical social network

Ayadi¹,

Bouslimi²,

Akaichi³

2016

Netw Model Anal Health Inform Bioinforma

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“…Arabic handwriting is considered one of the large alphabet language and therefore poses serious issues during Arabic writer identification process (Saba et al, 2011b;Saba et al, 2016). Initially, writer identification systems for Latin scripts were directly applied for Arabic writer identification brought low accuracy (Soleimanizadeh et al, 2015;Younus et al, 2015). Hence, researchers concluded that Arabic writer identification accuracy is far from maturity and continuous efforts are in demand from the research community.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy ARTMAP Approach for Arabic Writer Identification using Novel Features Fusion

Saba

2018

Journal of Computer Science

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Arabic writer identification and associated tasks are still fresh due to huge variety of Arabic writer's styles. Current research presents a fusion of statistical features, extracted from fragments of Arabic handwriting samples to identify the writer using fuzzy ARTMAP classifier. Fuzzy ARTMP is supervised neural model, especially suited to classification problems. It is faster to train and need less number of training epochs to "learn" from input data for generalization. The extracted features are fed to Fuzzy ARTMP for training and testing. Fuzzy ARTMAP is employed for the first time along with a novel fusion of statistical features for Arabic writer identification. The entire IFN/ENIT database is used in experiments such that 75% handwritten Arabic words from 411 writers are employed in training and 25% for testing the system at random. Several combinations of extracted features are tested using fuzzy ARTMAP classifier and finally one combination exhibited promising accuracy of 94.724% for Arabic writer identification on IFN/ENIT benchmark database.

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