The effectiveness of image features based on fractal image coding for image annotation

Shih, Cho-Wei; Chu, Hui-Chuan; Chen, Yuh‐Min; Wen, Chuin-Cheng

doi:10.1016/j.eswa.2012.05.003

Cited by 8 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It allows for swift and effective query, retrieval, and organization of image information. AIA has applications in various areas, including online and offline data exploration, image manipulation, and mobile annotation tools [25][26][27][28].…”

Section: Image Annotationmentioning

confidence: 99%

Automatic Image Annotation (AIA) of AlmondNet-20 Method for Almond Detection by Improved CNN-Based Model

Asghari Ilani,

Moftakhar Tehran,

Kavei

et al. 2024

Preprint

View full text Add to dashboard Cite

In response to the burgeoning global demand for premium agricultural products, particularly within the competitive nut market, this paper introduces an innovative methodology aimed at enhancing the grading process for almonds and their shells. Leveraging state-of-the-art Deep Convolutional Neural Networks (CNNs), specifically the AlmondNet-20 architecture, our study achieves exceptional accuracy exceeding 99%, facilitated by the utilization of a 20-layer CNN model. To bolster robustness in differentiating between almonds and shells, data augmentation techniques are employed, ensuring the reliability and accuracy of our classification system. Our model, meticulously trained over 1000 epochs, demonstrates remarkable performance, boasting an accuracy rate of 99% alongside a minimal loss function of 0.0567. Rigorous evaluation through test datasets further validates the efficacy of our approach, revealing impeccable precision, recall, and F1-score metrics for almond detection. Beyond its technical prowess, this advanced classification system offers tangible benefits to both industry experts and non-specialists alike, ensuring globally reliable almond classification. The application of deep learning algorithms, as showcased in our study, not only enhances grading accuracy but also presents opportunities for product patents, thereby contributing to the economic value of our nation. Through the adoption of cutting-edge technologies such as the AlmondNet-20 model, we pave the way for future advancements in agricultural product classification, ultimately enriching global trade and economic prosperity.

show abstract

Section: Image Annotationmentioning

confidence: 99%

Automatic Image Annotation (AIA) of AlmondNet-20 Method for Almond Detection by Improved CNN-Based Model

Asghari Ilani,

Moftakhar Tehran,

Kavei

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…There are publications indicating the expediency of such a solution (Cao, 2006;Compton & Ernstberger, 2020;Swathi et al, 2017), however, they are superficial, conducted on single images, without studying the real impact of SVD application on compression ratio, so those publications do not fully cover the subject. The main purpose of image compression is to reduce the space required for image storage so it is possible to minimize the amount of hardware needed to store images, which ultimately means a significant decrease in expenditure on storage of such data (Pratt, Kane & Andrews, 1969;Shih et al, 2012). It is also worth mentioning that thanks to smaller file size, the time needed for its transmission is reduced, meaning less bandwidth used which, of course, also reduces costs while increasing productivity.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Possibility of Using the Singular Value Decomposition in Image Compression

Lukasik

ŁABUĆ²

2022

acs

View full text Add to dashboard Cite

In today’s highly computerized world, data compression is a key issue to minimize the costs associated with data storage and transfer. In 2019, more than 70% of the data sent over the network were images. This paper analyses the feasibility of using the SVD algorithm in image compression and shows that it improves the efficiency of JPEG and JPEG2000 compression. Image matrices were decomposed using the SVD algorithm before compression. It has also been shown that as the image dimensions increase, the fraction of eigenvalues that must be used to reconstruct the image in good quality decreases. The study was carried out on a large and diverse set of images, more than 2500 images were examined. The results were analyzed based on criteria typical for the evaluation of numerical algorithms operating on matrices and image compression: compression ratio, size of compressed file, MSE, number of bad pixels, complexity, numerical stability, easiness of implementation.

show abstract

“…In the AIA, the main idea is to automatically learn the semantic concept models from a huge number of image samples and utilize the conceptual models to label new images with proper tags [1]. The AIA has a lot of applications in various fields including access, search, and navigate the huge amount of visual data which stored in online or offline data sources, image manipulation and annotation application that used on a mobile device, [2]- [4]. The typical image annotation approaches rely on human viewpoints and the performance of them is highly dependent on the inefficient manual operations.…”

Section: Introductionmentioning

confidence: 99%

An Effective Automatic Image Annotation Model Via Attention Model and Data Equilibrium

Vatani¹,

Ahvanooey²,

Rahimi³

2018

ijacsa

View full text Add to dashboard Cite

Nowadays, a huge number of images are available. However, retrieving a required image for an ordinary user is a challenging task in computer vision systems. During the past two decades, many types of research have been introduced to improve the performance of the automatic annotation of images, which are traditionally focused on content-based image retrieval. Although, recent research demonstrates that there is a semantic gap between content-based image retrieval and image semantics understandable by humans. As a result, existing research in this area has caused to bridge the semantic gap between low-level image features and high-level semantics. The conventional method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, we propose a novel AIA model based on the deep learning feature extraction method. The proposed model has three phases, including a feature extractor, a tag generator, and an image annotator. First, the proposed model extracts automatically the high and low-level features based on dual tree continues wavelet transform (DT-CWT), singular value decomposition, distribution of color ton, and the deep neural network. Moreover, the tag generator balances the dictionary of the annotated keywords by a new logentropy auto-encoder (LEAE) and then describes these keywords by word embedding. Finally, the annotator works based on the long-short-term memory (LSTM) network in order to obtain the importance degree of specific features of the image. The experiments conducted on two benchmark datasets confirm that the superiority of proposed model compared to the previous models in terms of performance criteria. Keywords-Automatic image annotation; attention model; skewed learning; deep learning, word embedding; log-entropy auto encoderI.

show abstract

The effectiveness of image features based on fractal image coding for image annotation

Cited by 8 publications

References 22 publications

Automatic Image Annotation (AIA) of AlmondNet-20 Method for Almond Detection by Improved CNN-Based Model

Automatic Image Annotation (AIA) of AlmondNet-20 Method for Almond Detection by Improved CNN-Based Model

Analysis of the Possibility of Using the Singular Value Decomposition in Image Compression

An Effective Automatic Image Annotation Model Via Attention Model and Data Equilibrium

Contact Info

Product

Resources

About