Searching Images in a Textile Image Database

Huang, Yin-Fu; Lin, Sheng-Min

doi:10.1007/978-3-319-11897-0_32

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…In contrast with the other papers described before, Chun et al carried out a retrieval system using a large dataset composed of 1343 textile images. In 2014, Huang and Lin [ 34 ] proposed a system based on the combination of color, texture and shape features in order to retrieve textiles over more than 4000 images downloaded from Globle-Tex Co., ( ). The retrieval system was based on a signature process extracted by different k-means clusters achieving an 83% of success rate.…”

Section: Related Researchmentioning

confidence: 99%

Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments

García-Olalla

Alegre

Fernández-Robles

et al. 2018

Sensors

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Textile based image retrieval for indoor environments can be used to retrieve images that contain the same textile, which may indicate that scenes are related. This makes up a useful approach for law enforcement agencies who want to find evidence based on matching between textiles. In this paper, we propose a novel pipeline that allows searching and retrieving textiles that appear in pictures of real scenes. Our approach is based on first obtaining regions containing textiles by using MSER on high pass filtered images of the RGB, HSV and Hue channels of the original photo. To describe the textile regions, we demonstrated that the combination of HOG and HCLOSIB is the best option for our proposal when using the correlation distance to match the query textile patch with the candidate regions. Furthermore, we introduce a new dataset, TextilTube, which comprises a total of 1913 textile regions labelled within 67 classes. We yielded 84.94% of success in the 40 nearest coincidences and 37.44% of precision taking into account just the first coincidence, which outperforms the current deep learning methods evaluated. Experimental results show that this pipeline can be used to set up an effective textile based image retrieval system in indoor environments.

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Section: Related Researchmentioning

confidence: 99%

Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments

García-Olalla

Alegre

Fernández-Robles

et al. 2018

Sensors

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“…In this paper, we adopt the bag-of-features MPEG-7 [1,2,3,4,13,14,22,23,24] defined by the MPEG organization, which consists of color description (i.e., two color descriptors), texture description (i.e., two texture descriptors), and shape description (i.e., one shape descriptor). Among them, the descriptors relevant to image characteristics are as follows and the numbers of their features are shown in Table 1 To extract CLD features, an image on the RGB color space is partitioned into 8*8=64 blocks first; then, the average color of each block is calculated and converted into YCbCr color; next, each Y, Cb, Cr color space is transformed by 8x8 DCT, so that three sets of 64 DCT coefficients are obtained; finally, a zigzag scanning is performed with these three sets of 64 DCT coefficients, and CLD features are extracted with the specified length 12 (i.e., 6 coefficients from Y, 3 coefficients from Cb, and 3 coefficients from Cr).…”

Section: Feature Extractionmentioning

confidence: 99%

Content-Based Image Retrieval System for Real Images

Huang¹,

Chen²

EPiC Series in Computing

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With the rapid progress of network technologies and multimedia data, information retrieval techniques gradually become content-based, and not text-based yet. In this paper, we propose a content-based image retrieval system to query similar images in a real image database. First, we employ segmentation and main object detection to separate the main object from an image. Then, we extract MPEG-7 features from the object and select relevant features using the SAHS algorithm. Next, two approaches "one-againstall" and "one-against-one" are proposed to build the classifiers based on SVM. To further reduce indexing complexity, K-means clustering is used to generate MPEG-7 signatures. Thus, we combine the classes predicted by the classifiers and the results based on the MPEG-7 signatures, and find out the similar images to a query image. Finally, the experimental results show that our method is feasible in image searching from the real image database and more effective than the other methods.

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Searching Images in a Textile Image Database

Cited by 2 publications

References 9 publications

Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments

Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments

Content-Based Image Retrieval System for Real Images

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