Review: Automatic Image Annotation for Semantic Image Retrieval

Abioui, Hasna; Idarrou, Ali; Bouzit, Ali; Mammass, Driss

doi:10.1007/978-3-319-94211-7_15

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…A lot of research has been done on TBIR, but are very ancient due to the great importance given to the other types of ImR. TBIR can be based on annotation [15]. The major drawback of this approach is that for a descriptive annotation, it must be manual, hence, the complexity of the task.…”

Section: Image Retrieval Techniquesmentioning

confidence: 99%

Graph-Based Image Retrieval: State of the Art

Belahyane

Mammass

Abioui

et al. 2020

Lecture Notes in Computer Science

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The paper deals with the problem of semantic Image Retrieval. Indeed, the image has recently gained popularity in several domains such as medical domain, marketing, etc. Image plays a very vital role in documentation. However, finding visual and relevant information in an image is a huge task for Image Retrieval community and a very discussed issue in digital image processing. In fact, image can be extracted from a big collection of images, in the purpose of responding to user's need. Image Retrieval processes based on classical techniques may not be sufficient to user. For several years, great efforts have been devoted to integrate semantic aspect, in order to enhance relevance of the result and ensure high-level content consideration in image. This paper presents a state of the art of Image Retrieval approaches using graph theory due to the growing interest given to graphs in terms of performance, representation and its ability to ingrate semantic aspect. We review a number of recently available graph-based approaches in Image Retrieval aiming to determine factors adding semantic aspect in Image Retrieval system.

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Section: Image Retrieval Techniquesmentioning

confidence: 99%

Graph-Based Image Retrieval: State of the Art

Belahyane

Mammass

Abioui

et al. 2020

Lecture Notes in Computer Science

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“…Therefore, such systems are considered very difficult to use and time-consuming, especially when the groups and classes of images are large. A summarization of the disadvantages of TBIR is found in Abioui et al (2018). In the late 1990s, due to the shortcomings of TBIR, a new method of content-based image retrieval (CBIR) was developed to solve the previous limitations (Rui et al, 1999), the latest method used in retrieving images from image repositories and websites reported in Smeulders et al (2000), and Marques and Furht (2002).…”

Section: Introductionmentioning

confidence: 99%

Pre-trained convolution neural networks models for content-based medical image retrieval

Ahmed¹,

Almagrabi²,

Osman³

2022

Int. j. adv. appl. sci.

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Content-based image retrieval (CBIR) is a recent method used to retrieve different types of images from repositories. The traditional content-based medical image retrieval (CBMIR) methods commonly used low-level image representation features extracted from color, texture, and shape image descriptors. Since most of these CBMIR systems depend mainly on the extracted features, the methods used in the feature extraction phase are more important. Features extraction methods, which generate inaccurate features, lead to very poor performance retrieval because of semantic gap widening. Hence, there is high demand for independent domain knowledge features extraction methods, which have automatic learning capabilities from input images. Pre-trained deep convolution neural networks (CNNs), the recent generation of deep learning neural networks, could be used to extract expressive and accurate features. The main advantage of these pre-trained CNNs models is the pre-training process for huge image data of thousands of different classes, and their knowledge after the training process could easily be transferred. There are many successful models of pre-trained CNNs models used in the area of medical image retrieval, image classification, and object recognition. This study utilizes two of the most known pre-trained CNNs models; ResNet18 and SqueezeNet for the offline feature extraction stage. Additionally, the highly accurate features extracted from medical images are used for the CBMIR method of medical image retrieval. This study uses two popular medical image datasets; Kvasir and PH2 to show that the proposed methods have good retrieval results. The retrieval performance evaluation measures of our proposed method have average precision of 97.75% and 83.33% for Kvasir and PH2 medical images respectively, and outperform some of the state-of-the-art methods in this field of study because these pre-trained CNNs have well trained layers among a huge number of image types. Finally, intensive statistical analysis shows that the proposed ResNet18-based retrieval method has the best performance for enhancing both recall and precision measures for both medical images.

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“…So, in order to extract image features with more discriminability and form effective image representation, a lot of research has been done on feature extraction algorithms. In recent ten years, it has experienced a development process from extracting shallow layer features based on Scale-invariant feature transform (SIFT) [1], speeded up robust features (SURF) [2] algorithms and embedding coding method in combination with bag of words (BOW) [3,4], fisher vector (FV) [5] and vector of local aggregated descriptors (VLAD) [6] to extracting deep layer features based on the deep convolutional neural network.…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature Aggregation Approach for Image Retrieval Using Local and Global Features

Li¹,

He²,

Ma³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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The current deep convolution features based on retrieval methods cannot fully use the characteristics of the salient image regions. Also, they cannot effectively suppress the background noises, so it is a challenging task to retrieve objects in cluttered scenarios. To solve the problem, we propose a new image retrieval method that employs a novel feature aggregation approach with an attention mechanism and utilizes a combination of local and global features. The method first extracts global and local features of the input image and then selects keypoints from local features by using the attention mechanism. After that, the feature aggregation mechanism aggregates the keypoints to a compact vector representation according to the scores evaluated by the attention mechanism. The core of the aggregation mechanism is to allow features with high scores to participate in residual operations of all cluster centers. Finally, we get the improved image representation by fusing aggregated feature descriptor and global feature of the input image. To effectively evaluate the proposed method, we have carried out a series of experiments on large-scale image datasets and compared them with other state-of-the-art methods. Experiments show that this method greatly improves the precision of image retrieval and computational efficiency.

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Review: Automatic Image Annotation for Semantic Image Retrieval

Cited by 6 publications

References 17 publications

Graph-Based Image Retrieval: State of the Art

Graph-Based Image Retrieval: State of the Art

Pre-trained convolution neural networks models for content-based medical image retrieval

A Novel Feature Aggregation Approach for Image Retrieval Using Local and Global Features

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