Color image indexing using BTC

Qiu, Guoping

doi:10.1109/tip.2002.807356

Cited by 127 publications

(8 citation statements)

References 20 publications

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“…Due to the rapid growth of the internet and advancements in image acquisition devices, increasing amounts of visual data are created and stored, leading to an exponential increase in the volume of image collections. The techniques have been introduced to improve the effectiveness as well as efficiency of the content-based image retrieval (CBIR) systems [ 1 – 5 ]. CBIR is the mechanism by which a system retrieves images from an image collection according to the visual contents of the query image.…”

Section: Introductionmentioning

confidence: 99%

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model

et al. 2018

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For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques.

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Section: Introductionmentioning

confidence: 99%

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model

et al. 2018

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“…The mostly used color spaces in the CBIR domain are HSV (LSV), YCbCr, RGB, and LAB. These color spaces are characterized using color moments (Duanmu, 2010), color correlogram (Huang et al, 1997), color histogram (Flickner et al, 1995), dominant color descriptor color co-occurrence matrix (Qiu, 2003), and many other descriptors. Color features are considered a robust feature because they are invariant against translation, rotation, and scale change (Shrivastava & Tyagi, 2015).…”

Section: Color Featurementioning

confidence: 99%

Content-based image retrieval: A review of recent trends

2021

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With the availability of internet technology and the low-cost of digital image sensor, enormous amount of image databases have been created in different kind of applications. These image databases increase the demand to develop efficient image retrieval search methods that meet user requirements. Great attention and efforts have been devoted to improve content-based image retrieval method with a particular focus on reducing the semantic gap between low-level features and human visual perceptions. Due to the increasing research in this field, this paper surveys, analyses and compares the current state-of-the-art methodologies over the last six years in the CBIR field. This paper also provides an overview of CBIR framework, recent low-level feature extraction methods, machine learning algorithms, similarity measures, and a performance evaluation to inspire further research efforts.

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“…The color histogram is one of the most well-known color features used for image feature extraction 23, 34 , which denotes the joint probability of the intensity of an image. From probability theory, a probability distribution can be uniquely characterized by its moments.…”

Section: Feature Extractionmentioning

confidence: 99%

Automatic Detection of Galaxy Type From Datasets of Galaxies Image Based on Image Retrieval Approach

Elaziz

Selim

Xiong

2017

Sci Rep

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This paper presents a new approach for the automatic detection of galaxy morphology from datasets based on an image-retrieval approach. Currently, there are several classification methods proposed to detect galaxy types within an image. However, in some situations, the aim is not only to determine the type of galaxy within the queried image, but also to determine the most similar images for query image. Therefore, this paper proposes an image-retrieval method to detect the type of galaxies within an image and return with the most similar image. The proposed method consists of two stages, in the first stage, a set of features is extracted based on shape, color and texture descriptors, then a binary sine cosine algorithm selects the most relevant features. In the second stage, the similarity between the features of the queried galaxy image and the features of other galaxy images is computed. Our experiments were performed using the EFIGI catalogue, which contains about 5000 galaxies images with different types (edge-on spiral, spiral, elliptical and irregular). We demonstrate that our proposed approach has better performance compared with the particle swarm optimization (PSO) and genetic algorithm (GA) methods.

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Color image indexing using BTC

Cited by 127 publications

References 20 publications

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model

Content-based image retrieval: A review of recent trends

Automatic Detection of Galaxy Type From Datasets of Galaxies Image Based on Image Retrieval Approach

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