Effective Image Retrieval System Using Dot-Diffused Block Truncation Coding Features

Guo, Jing-Ming; Prasetyo, Heri; Wang, Nai-Jian

doi:10.1109/tmm.2015.2449234

Cited by 48 publications

(34 citation statements)

References 73 publications

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“…The second remark is that, as previously mentioned in the introduction of our article, the local patterns-based schemes (such as LtrP [17], LECoP [21], etc.) and the OTB-based systems [26][27][28] as well as the recently proposed learned descriptors based on pre-trained CNNs [50,51] generally provide higher ARR than the wavelet-based probabilistic approaches [4][5][6][7][8][9]11,12,29]. Then, more importantly, our LED+RD framework (both the 27D version and the improved 33D version) has outperformed all reference methods for all the three databases.…”

Section: Performance In Retrieval Accuracymentioning

confidence: 90%

“…The first BTC-based retrieval scheme for color images was proposed in [22] followed by some improvements a few years later [23,24]. Until very recently, one has been witnessing the evolution of BTC-based retrieval frameworks such as the ordered-dither BTC (ODBTC) [25,26], the error diffusion BTC (EDBTC) [27] and the dot-diffused BTC (DDBTC) [28]. Within these approaches, an image is divided into multiple non-overlapping blocks and one of the BTC-based systems compresses each block into the so-called color quantizer and bitmap image.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the fact that natural images usually involve a variety of local textures and structures that do not appear homogeneous within the entire image, an approach taking into account local features could become relevant. This may be the reason why most local feature-based CBIR schemes (e.g., LTrP [17], LOCTP [20], LECoP [21]) or BTC-based approaches [25][26][27][28] (i.e., which, in fact, sub-divide each query image into multiple blocks) have achieved better retrieval performance than probabilistic methods, which model the entire image using different statistical distributions [4][5][6][7][8][9]11,12,29]. We will provide later their performance for a comparison within the experimental study.…”

Section: Introductionmentioning

confidence: 99%

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Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

2017

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A novel efficient method for content-based image retrieval (CBIR) is developed in this paper using both texture and color features. Our motivation is to represent and characterize an input image by a set of local descriptors extracted from characteristic points (i.e., keypoints) within the image. Then, dissimilarity measure between images is calculated based on the geometric distance between the topological feature spaces (i.e., manifolds) formed by the sets of local descriptors generated from each image of the database. In this work, we propose to extract and use the local extrema pixels as our feature points. Then, the so-called local extrema-based descriptor (LED) is generated for each keypoint by integrating all color, spatial as well as gradient information captured by its nearest local extrema. Hence, each image is encoded by an LED feature point cloud and Riemannian distances between these point clouds enable us to tackle CBIR. Experiments performed on several color texture databases including Vistex, STex, color Brodazt, USPtex and Outex TC-00013 using the proposed approach provide very efficient and competitive results compared to the state-of-the-art methods.

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Section: Performance In Retrieval Accuracymentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

2017

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“…For the low-level features, only some key information is extracted from the images. The former works, [3], [12]- [14] and [27]- [28], mainly utilized the compressed stream such as BTC, JPEG and DCT to assemble the features. Some of the halftoning-based BTC compressions (EDBTC, ODBTC, DDBTC) are having good image quality, to extract the important features for efficient image retrieval.…”

Section: Feature Extraction For Cbirmentioning

confidence: 99%

“…The higher value of APR and ARR exhibits the better image retrieval performance The APR and ARR [14] are formally defined as APR =…”

Section: Average Precision Rate (Apr) and Average Retrieval Rate (Arr)mentioning

confidence: 99%

A survey on Block Truncation Coding based Content Based Image Retrieval

Sutha¹,

Mahendran²,

Peter³

2017

IJCA

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Content Based Image Retrieval (CBIR) is an important active research topic helps the user to search the relevant images from the large image collections by analyzing the content of the user required query image. With the explosive growth of image databases over internet and the availability of image capturing devices like mobile phones, digital camera and scanners, efficient image retrieval techniques are required. Many researchers proposed various techniques to improve the retrieval accuracy of content based image retrieval in a reasonable amount of time. When compared to other image compression techniques, the Block truncation coding (BTC) has been considered as a very efficient compression technique which requires least computational complexity and it can also effectively employed to index images in database for CBIR applications. This paper attempts to provide a comprehensive survey on various image retrieval techniques using BTC compression based indexing techniques. Further, some other issues, retrieval performance metrics and future enhancements are also discussed.

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Content‐based image retrieval for super‐resolutioned images using feature fusion: Deep learning and hand crafted

Pathak

Raju

2022

Concurrency and Computation

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An innovative image retrieval agenda by concatenating deep learning features from GoogleNet and low‐level features from HSI and RGB color space is proposed in this article. Most of the CNN features suffer from loss of information due to image resize as a pre‐processing stage. To reduce this information loss super‐resolution technic is used for resizing images. An improved form of dot‐diffused block truncation coding is used for extracting RGB handcraft features. To discover the interdependencies between color and intensity component of an image, interchannel voting between hue, saturation, and intensity component is calculated as a color feature in HSI space. Histogram of orientated gradient (HOG) feature is used as shape feature. Five standard performance parameters, average precision rate, average recall rate, F‐Measure, Average Normalized Modified Retrieval Rank, and Total Minimum Retrieval Epoch, are applied on nine image datasets: Corel‐1K, Corel‐5K, Corel‐10K, VisTex, STex, ColorBrodatz and three subsets of ImageNet dataset for evaluation process of proposed method. For all dataset the best performance is achieved by the proposed method with respect to all performance parameters.

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Effective Image Retrieval System Using Dot-Diffused Block Truncation Coding Features

Cited by 48 publications

References 73 publications

Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

A survey on Block Truncation Coding based Content Based Image Retrieval

Content‐based image retrieval for super‐resolutioned images using feature fusion: Deep learning and hand crafted

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