Recent evaluation on Content Based Image Retrieval

Chavda, Sagar; Goyani, Mahesh

doi:10.26438/ijcse/v7i4.325329

Cited by 5 publications

(3 citation statements)

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“…Before performing the search, the content-based image retrieval system extracts features from the database images and describes the images using feature vectors so that there is a one-to-one correspondence between the images and the feature vectors, and finally returns the retrieved results by calculating and sorting the distance between the feature vectors. Therefore, retrieval performance is closely related to feature extraction, feature selection, and similarity measurement of matched images [19]. Among these, feature extraction and selection are the most influential factors in representing images' semantic content.…”

Section: Content-based Image Retrievalmentioning

confidence: 99%

Image Retrieval Method Based on Visual Map Pre-Sampling Construction in Indoor Positioning

Bai

Qin

Zheng

et al. 2023

IJGI

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In visual indoor positioning systems, the method of constructing a visual map by point-by-point sampling is widely used due to its characteristics of clear static images and simple coordinate calculation. However, too small a sampling interval will cause image redundancy, while too large a sampling interval will lead to the absence of any scene images, which will result in worse positioning efficiency and inferior positioning accuracy. As a result, this paper proposed a visual map construction method based on pre-sampled image features matching, according to the epipolar geometry of adjacent position images, to determine the optimal sampling spacing within the constraints and effectively control the database size while ensuring the integrity of the image information. In addition, in order to realize the rapid retrieval of the visual map and reduce the positioning error caused by the time overhead, an image retrieval method based on deep hashing was also designed in this paper. This method used a convolutional neural network to extract image features to construct the semantic similarity structure to guide the generation of hash code. Based on the log-cosh function, this paper proposed a loss function whose function curve was smooth and not affected by outliers, and then integrated it into the deep network to optimize parameters, for fast and accurate image retrieval. Experiments on the FLICKR25K dataset and the visual map proved that the method proposed in this paper could achieve sub-second image retrieval with guaranteed accuracy, thereby demonstrating its promising performance.

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Section: Content-based Image Retrievalmentioning

confidence: 99%

Image Retrieval Method Based on Visual Map Pre-Sampling Construction in Indoor Positioning

Bai

Qin

Zheng

et al. 2023

IJGI

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“…Therefore, tissue classification has been considered by many scientific researchers in the last decade. Tissue characteristics can be used in many displacement vision problems and machine learning, such as medical image recognition [2][3], image retrieval [4], object recognition [5], skin recognition [6] and ..... In the last decade, texture classification and texture analysis have been an important topic in the literature on computer vision and image processing sciences.…”

Section: Introductionmentioning

confidence: 99%

Image classification using concatenated of co-occurrence matrix features and local ternary patterns

Kiani

2021

Preprint

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Texture, color, and shape are the three main components that the human visual brain uses to identify or identify environments and objects. Therefore, tissue classification has been considered by many scientific researchers in the last decade. The texture features can be used in many different vision and machine learning problems. As of now, various methods have been proposed for classifying tissues. In all methods, the accuracy of the classification is a major challenge that needs to be improved. This article presents a new method based on a combination of two efficient tissue descriptors, the co-occurrence matrix and local ternary patterns (LTP). First, the local binary pattern and LTP are performed to extract information from the local tissue. In the next step, a subset of statistical properties is extracted from the gray surface concurrency matrices. Finally, the interconnected features are used to teach classification. Performance is evaluated for accuracy on the Brodatz reference data set. The experimental results show that the proposed method offers a higher degree of classification compared to some advanced methods.

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“…The human visual system typically uses color, shape, and texture parameters to identify the contents of images. Therefore, extracting texture, color and shape features is very important in computer vision [1]. So far, various operators have been proposed to extract texture and color information.…”

Section: Introductionmentioning

confidence: 99%

Combination of Texture, Color and Shape Operators to Describe Image Content: A Survey

Ardestani¹

2020

Preprint

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In many image processing and computer vision applications, the main aim is to describe image contents. So, different visual properties such as color, texture and shape are extracted to make aim. In this respect, texture information play important role in image description and visual pattern classification. Texture is referred to a specific local distribution of intensities that is repeated throughout the image. Since now different operations or descriptors have been proposed to analysis texture characteristics. In the multi object images specific texture operators usually doesn’t provide accurate results. So, in many cases, combination of texture operators are used to achieve more discriminant features. In this paper, some combination methods are survived to analysis effect of combinational texture features in image content description. Also, in the result part, different related methods are compared in terms of accuracy and computational complexity.

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Recent evaluation on Content Based Image Retrieval

Cited by 5 publications

References 0 publications

Image Retrieval Method Based on Visual Map Pre-Sampling Construction in Indoor Positioning

Image Retrieval Method Based on Visual Map Pre-Sampling Construction in Indoor Positioning

Image classification using concatenated of co-occurrence matrix features and local ternary patterns

Combination of Texture, Color and Shape Operators to Describe Image Content: A Survey

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