An overview of cluster-based image search result organization: background, techniques, and ongoing challenges

Tekli, Joe

doi:10.1007/s10115-021-01650-9

Cited by 22 publications

(10 citation statements)

References 217 publications

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“…A multicolour digital image can be represented by three components 11 : red, green and blue (RGB) and the shades of each colour can be represented by bits (1byte). A digital image is therefore a function of the type: where D is a spatial domain composed of coordinates in a sampling grid, each element of which is called a pixel.…”

Section: Methodsmentioning

confidence: 99%

Quantifying root colonization by a symbiotic fungus using automated image segmentation and machine learning approaches

Sciascia,

Crosino,

Genre

2023

Sci Rep

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Arbuscular mycorrhizas (AM) are one of the most widespread symbiosis on earth. This plant-fungus interaction involves around 72% of plant species, including most crops. AM symbiosis improves plant nutrition and tolerance to biotic and abiotic stresses. The fungus, in turn, receives carbon compounds derived from the plant photosynthetic process, such as sugars and lipids. Most studies investigating AM and their applications in agriculture requires a precise quantification of the intensity of plant colonization. At present, the majority of researchers in the field base AM quantification analyses on manual visual methods, prone to operator errors and limited reproducibility. Here we propose a novel semi-automated approach to quantify AM fungal root colonization based on digital image analysis comparing three methods: (i) manual quantification (ii) image thresholding, (iii) machine learning. We recognize machine learning as a very promising tool for accelerating, simplifying and standardizing critical steps in analysing AM quantification, answering to an urgent need by the scientific community studying this symbiosis.

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Section: Methodsmentioning

confidence: 99%

Quantifying root colonization by a symbiotic fungus using automated image segmentation and machine learning approaches

Sciascia,

Crosino,

Genre

2023

Sci Rep

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“…For a user intending to search for an Apple computer, the user may be hampered by results for edible apples supplied by the image search engine [9,10]. To solve these problems, many strategies involving image search results clustering (ISRC) are proposed [11,12]. Most of the strategies use image analyses and text mining to derive the characteristics of the images in the retrieved web pages.…”

Section: Introductionmentioning

confidence: 99%

“…With these characteristics, ISRC strategies can cluster the images into several clusters and then display them to users for easy browsing. [11,12]. Most of the strategies use image analyses and text mining to derive the characteristics of the images in the retrieved web pages.…”

Section: Introductionmentioning

confidence: 99%

Improving the Precision of Image Search Engines with the Psychological Intention Diagram

Wu,

Lin

2024

Electronics

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With the increase in the amount of images online, the whole Internet is becoming an image database. Since there are so many available images, it is difficult for users to find the desired images. Unlike text search engines, image search engines cannot fully recognize the visual meaning of an image. In addition, it is difficult to obtain the desired images from the keywords provided by the user, since a keyword may contain multiple meanings. To solve these problems, this paper proposes a psychological intention diagram of past users, if inquiring using a keyword, to predict the images that these users want. Based upon the novel psychological diagram, this paper proposes a search engine that analyzes images in the sequential probing of the current user if he/she inquires after the same keywords as previous users. Moreover, this paper also constructs a psychological intention diagram of the designers of the web pages containing the keyword. This type of psychological intention diagram is used when a query is not issued by past users. To the best of our knowledge, this paper is the first one considering the psychological viewpoint of users and web designers in guiding the retrieval of the search engine. The experimental results show that the proposed image search engine has high precision; therefore, the method of providing images can help users to find their desired image more easily.

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“…Generally, each view of these datasets can be represented by distinct descriptors, such as color, edges, texture, etc. The application of multi-view data analysis is crucial in various fields, including recommender systems, medical diagnosis, image segmentation [1][2][3], image search result organization (ISRO) [4] and skew distribution datasets clustering [5]. However, when the dataset has high dimensionality and a large number of samples, several clustering methods can be limited when applying to these applications.…”

Section: Introductionmentioning

confidence: 99%

Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix

Shi,

Zhao

2023

Applied Sciences

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Deep Multi-view Subspace Clustering is a powerful unsupervised learning technique for clustering multi-view data, which has achieved significant attention during recent decades. However, most current multi-view clustering methods rely on learning self-expressive layers to obtain the ultimate clustering results, where the size of the self-expressive matrix increases quadratically with the number of input data points, making it difficult to handle large-scale datasets. Moreover, since multiple views are rich in information, both consistency and specificity of the input images need to be considered. To solve these problems, we propose a novel deep multi-view clustering approach based on the reconstructed self-expressive matrix (DCRSM). We use a reconstruction module to approximate self-expressive coefficients using only a small number of training samples, while the conventional self-expressive model must train the network with entire datasets. We also use shared layers and specific layers to integrate consistent and specific information of features to fuse information between views. The proposed DCRSM is extensively evaluated on multiple datasets, including Fashion-MNIST, COIL-20, COIL-100, and YTF. The experimental results demonstrate its superiority over several existing multi-view clustering methods, achieving an improvement between 1.94% and 4.2% in accuracy and a maximum improvement of 4.5% in NMI across different datasets. Our DCRSM also yields competitive results even when trained by 50% samples of the whole datasets.

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An overview of cluster-based image search result organization: background, techniques, and ongoing challenges

Cited by 22 publications

References 217 publications

Quantifying root colonization by a symbiotic fungus using automated image segmentation and machine learning approaches

Quantifying root colonization by a symbiotic fungus using automated image segmentation and machine learning approaches

Improving the Precision of Image Search Engines with the Psychological Intention Diagram

Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix

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