Fractal-based texture and HSV color features for fabric image retrieval

Suciati, Nanik; Herumurti, Darlis; Wijaya, Arya Yudhi

doi:10.1109/iccsce.2015.7482180

Cited by 23 publications

(24 citation statements)

References 14 publications

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“…Conversely, the retrieval time is longer than state of the art, therefore, to improve the accuracy of printed image retrieval, the authors combined color, texture, and spatial information features of fabric images. The research focused on fractal image capture, and color features [13] with HSV used to provide color features. Fractal-based texture features of batik image represent fractal dimension and lacunarity.…”

Section: Feature Extraction Based On Traditional Methodsmentioning

confidence: 99%

Content-based image retrieval for fabric images: A survey

Tena

Hartanto

Ardiyanto

2021

IJEECS

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In <span>recent years, a great deal of research has been conducted in the area of fabric image retrieval, especially the identification and classification of visual features. One of the challenges associated with the domain of content-based image retrieval (CBIR) is the semantic gap between low-level visual features and high-level human perceptions. Generally, CBIR includes two main components, namely feature extraction and similarity measurement. Therefore, this research aims to determine the content-based image retrieval for fabric using feature extraction techniques grouped into traditional methods and convolutional neural networks (CNN). Traditional descriptors deal with low-level features, while CNN addresses the high-level, called semantic features. Traditional descriptors have the advantage of shorter computation time and reduced system requirements. Meanwhile, CNN descriptors, which handle high-level features tailored to human perceptions, deal with large amounts of data and require a great deal of computation time. In general, the features of a CNN's fully connected layers are used for matching query and database images. In several studies, the extracted features of the CNN's convolutional layer were used for image retrieval. At the end of the CNN layer, hash codes are added to reduce </span>search time.

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Section: Feature Extraction Based On Traditional Methodsmentioning

confidence: 99%

Content-based image retrieval for fabric images: A survey

Tena

Hartanto

Ardiyanto

2021

IJEECS

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“…Since conventional color histogram [10] has a high feature dimension, and thus is not conducive to classification, an improved color histogram method is proposed and used to extract the color features of the CEIs. It is known that the HSV color space is a kind of natural representation color model and thus can better reflect the physiological perception of the human eyes [11]. Therefore, it is proposed that the H, S, and V components are quantified nonuniformly according to the color perception characteristics of humans in the HSV color space.…”

Section: Extracting Color Featuresmentioning

confidence: 99%

Study on the classification of capsule endoscopy images

et al. 2019

J Image Video Proc.

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Wireless capsule endoscope allows painless endoscopic imaging of the gastrointestinal track of humans. However, the whole procedure will generate a large number of capsule endoscopy images (CEIs) for reading and recognizing. In order to save the time and energy of physicians, computer-aided analysis methods are imperatively needed. Due to the influence of air bubble, illumination, and shooting angle, however, it is difficult to classify CEIs into healthy and diseased categories correctly for a conventional classification method. To this end, in the paper, a new feature extraction method is proposed based on color histogram, wavelet transform, and co-occurrence matrix. First, an improved color histogram is calculated in the HSV (hue, saturation, value) space. Meanwhile, by using the wavelet transform, the low-frequency parts of the CEIs are filtered out, and then, the characteristic values of the reconstructed CEIs' co-occurrence matrix are calculated. Next, by employing the proposed feature extraction method and the BPNN (back propagation neural network), a novel computer-aided classification algorithm is developed, where the feature values of color histogram and co-occurrence matrix are normalized as the inputs of the BPNN for training and classification. Experimental results show that the accuracy of the proposed algorithm is up to 99.12% which is much better than the compared conventional methods.

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“…Image preprocessing is the first stage of detection in order to improve the quality of images with color metric extraction and normalization. Color Metric Extraction reduced computational burdens and quantization of color can be used to represent images without significantly reducing image quality [15].…”

Section: Pre-processingmentioning

confidence: 99%

Content-Dependent Image Search System for Aggregation of Color, Shape and Texture Features

Kurniasari¹,

Barakbah²,

Basuki³

2019

emitter

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The existing image search system often faces difficulty to find a appropriate retrieved image corresponding to an image query. The difficulty is commonly caused by that the usersâ€™ intention for searching image is different with dominant information of the image collected from feature extraction. In this paper we present a new approach for content-dependent image search system. The system utilizes information of color distribution inside an image and detects a cloud of clustered colors as something - supposed as an object. We applies segmentation of image as content-dependent process before feature extraction in order to identify is there any object or not inside an image. The system extracts 3 features, which are color, shape, and texture features and aggregates these features for similarity measurement between an image query and image database. HSV histogram color is used to extract color feature of image. While the shape feature extraction used Connected Component Labeling (CCL) which is calculated the area value, equivalent diameter, extent, convex hull, solidity, eccentricity, and perimeter of each object. The texture feature extraction used Leung Malik (LM)â€™s approach with 15 kernels.Â For applicability of our proposed system, we applied the system with benchmark 1000 image SIMPLIcity dataset consisting of 10 categories namely Africans, beaches, buildings historians, buses, dinosaurs, elephants, roses, horses, mountains, and food. The experimental results performed 62% accuracy rate to detect objects by color feature, 71% by texture feature, 60% by shape feature, 72% by combined color-texture feature, 67% by combined color-shape feature, 72 % combined texture-shape features and 73% combined all features.

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Fractal-based texture and HSV color features for fabric image retrieval

Cited by 23 publications

References 14 publications

Content-based image retrieval for fabric images: A survey

Content-based image retrieval for fabric images: A survey

Study on the classification of capsule endoscopy images

Content-Dependent Image Search System for Aggregation of Color, Shape and Texture Features

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