Using Image Segmentation in Content Based Image Retrieval Method

Ouhda, Mohamed; Asnaoui, Khalid El; Ouanan, Mohammed; Aksasse, Brahim

doi:10.1007/978-3-319-69137-4_17

Cited by 9 publications

(1 citation statement)

References 8 publications

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“…Image segmentation is a complex and difficult preprocessing step that plays an important role in automatic visual systems. It has a wide range of applications such as biometrics [ 6 , 7 , 8 ], medical image analysis [ 9 , 10 , 11 ], disease detection and classification in cultures [ 12 , 13 , 14 , 15 , 16 ], traffic control systems [ 1 , 17 , 18 , 19 , 20 ], self driving cars [ 21 , 22 , 23 , 24 ], locating objects in satellite images [ 25 ], and content image retrieval systems [ 26 , 27 , 28 , 29 ], among others.…”

Section: Introductionmentioning

confidence: 99%

Graphical Image Region Extraction with K-Means Clustering and Watershed

Jardim

António²,

Mora

2022

J. Imaging

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With a wide range of applications, image segmentation is a complex and difficult preprocessing step that plays an important role in automatic visual systems, which accuracy impacts, not only on segmentation results, but directly affects the effectiveness of the follow-up tasks. Despite the many advances achieved in the last decades, image segmentation remains a challenging problem, particularly, the segmenting of color images due to the diverse inhomogeneities of color, textures and shapes present in the descriptive features of the images. In trademark graphic images segmentation, beyond these difficulties, we must also take into account the high noise and low resolution, which are often present. Trademark graphic images can also be very heterogeneous with regard to the elements that make them up, which can be overlapping and with varying lighting conditions. Due to the immense variation encountered in corporate logos and trademark graphic images, it is often difficult to select a single method for extracting relevant image regions in a way that produces satisfactory results. Many of the hybrid approaches that integrate the Watershed and K-Means algorithms involve processing very high quality and visually similar images, such as medical images, meaning that either approach can be tweaked to work on images that follow a certain pattern. Trademark images are totally different from each other and are usually fully colored. Our system solves this difficulty given it is a generalized implementation designed to work in most scenarios, through the use of customizable parameters and completely unbiased for an image type. In this paper, we propose a hybrid approach to Image Region Extraction that focuses on automated region proposal and segmentation techniques. In particular, we analyze popular techniques such as K-Means Clustering and Watershedding and their effectiveness when deployed in a hybrid environment to be applied to a highly variable dataset. The proposed system consists of a multi-stage algorithm that takes as input an RGB image and produces multiple outputs, corresponding to the extracted regions. After preprocessing steps, a K-Means function with random initial centroids and a user-defined value for k is executed over the RGB image, generating a gray-scale segmented image, to which a threshold method is applied to generate a binary mask, containing the necessary information to generate a distance map. Then, the Watershed function is performed over the distance map, using the markers defined by the Connected Component Analysis function that labels regions on 8-way pixel connectivity, ensuring that all regions are correctly found. Finally, individual objects are labelled for extraction through a contour method, based on border following. The achieved results show adequate region extraction capabilities when processing graphical images from different datasets, where the system correctly distinguishes the most relevant visual elements of images with minimal tweaking.

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Section: Introductionmentioning

confidence: 99%

Graphical Image Region Extraction with K-Means Clustering and Watershed

Jardim

António²,

Mora

2022

J. Imaging

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Content‐based image retrieval using color and a novel texture descriptor: orientational‐based local binary pattern

Baarab

Chaouki

Masmoudi

2022

Concurrency and Computation

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Summary The increasing use of images and multimedia has led, in the past years, to a major growth of data and images to store. With this, comes the importance of having a way to facilitate the indexing and the retrieval of the images. In this article, we present a new method for content‐based image retrieval. The proposed approach is composed of two phases: first, a preselection of relevant images from the initial database based on HSV color space and using color moment information. The second phase relies on using the selected images from the first phase as a new database and extracting the texture feature using a novel descriptor called orientational‐based local binary pattern (OB‐LBP) derived from the traditional LBP texture feature technique. In the proposed descriptor (OB‐LBP) we compare each neighbor of the center pixel (of 3 × 3 window) with its three closest neighbors and form a binary code of three components for each pixel, then based on their directions we generate three images and produce the histogram to extract the final feature descriptor. Corel‐10 k and Wang's databases were used to evaluate the proposed method. The results show a significant improvement in the proposed method over existing methods.

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Automated Methods for Detection and Classification Pneumonia Based on X-Ray Images Using Deep Learning

Asnaoui¹,

Youness

Idri

2021

Studies in Big Data

Self Cite

146

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Recently, researchers, specialists, and companies around the world are rolling out deep learning and image processing-based systems that can fastly process hundreds of X-Ray and computed tomography (CT) images to accelerate the diagnosis of pneumonia such as SARS, COVID-19, and aid in its containment. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as MERS, COVID-19. In this paper, we present a comparison of recent Deep Convolutional Neural Network (DCNN) architectures for automatic binary classification of pneumonia images based fined tuned versions of (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, MobileNet_V2 and Xception). The proposed work has been tested using chest X-Ray & CT dataset which contains 5856 images (4273 pneumonia and 1583 normal). As result we can conclude that finetuned version of Resnet50, MobileNet_V2 and Inception_Resnet_V2 show highly satisfactory performance with rate of increase in training and validation accuracy (more than 96% of accuracy). Unlike CNN, Xception, VGG16, VGG19, Inception_V3 and DenseNet201 display low performance (more than 84% accuracy).

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Using Image Segmentation in Content Based Image Retrieval Method

Cited by 9 publications

References 8 publications

Graphical Image Region Extraction with K-Means Clustering and Watershed

Graphical Image Region Extraction with K-Means Clustering and Watershed

Content‐based image retrieval using color and a novel texture descriptor: orientational‐based local binary pattern

Automated Methods for Detection and Classification Pneumonia Based on X-Ray Images Using Deep Learning

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