A Hierarchical Task Assignment for Manual Image Labeling

Chang, Chia–Ming; Mishra, Siddharth; Igarashi, Takeo

doi:10.1109/vlhcc.2019.8818828

Cited by 8 publications

(2 citation statements)

References 13 publications

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“…Among the myriad of techniques used for data annotation, manual labeling stands out as a fundamental approach for enhancing the performance of AI models [3,4]. While extensive research has been conducted on manual labeling techniques and their impact on text classification, a critical gap remains in understanding the influence of data sorting methods on the quality of manual labeling for hierarchical classification tasks [5,6].…”

Section: Introductionmentioning

confidence: 99%

Data Sorting Influence on Short Text Manual Labeling Quality for Hierarchical Classification

Narushynska,

Teslyuk,

Doroshenko

et al. 2024

BDCC

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The precise categorization of brief texts holds significant importance in various applications within the ever-changing realm of artificial intelligence (AI) and natural language processing (NLP). Short texts are everywhere in the digital world, from social media updates to customer reviews and feedback. Nevertheless, short texts’ limited length and context pose unique challenges for accurate classification. This research article delves into the influence of data sorting methods on the quality of manual labeling in hierarchical classification, with a particular focus on short texts. The study is set against the backdrop of the increasing reliance on manual labeling in AI and NLP, highlighting its significance in the accuracy of hierarchical text classification. Methodologically, the study integrates AI, notably zero-shot learning, with human annotation processes to examine the efficacy of various data-sorting strategies. The results demonstrate how different sorting approaches impact the accuracy and consistency of manual labeling, a critical aspect of creating high-quality datasets for NLP applications. The study’s findings reveal a significant time efficiency improvement in terms of labeling, where ordered manual labeling required 760 min per 1000 samples, compared to 800 min for traditional manual labeling, illustrating the practical benefits of optimized data sorting strategies. Comparatively, ordered manual labeling achieved the highest mean accuracy rates across all hierarchical levels, with figures reaching up to 99% for segments, 95% for families, 92% for classes, and 90% for bricks, underscoring the efficiency of structured data sorting. It offers valuable insights and practical guidelines for improving labeling quality in hierarchical classification tasks, thereby advancing the precision of text analysis in AI-driven research. This abstract encapsulates the article’s background, methods, results, and conclusions, providing a comprehensive yet succinct study overview.

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Section: Introductionmentioning

confidence: 99%

Data Sorting Influence on Short Text Manual Labeling Quality for Hierarchical Classification

Narushynska,

Teslyuk,

Doroshenko

et al. 2024

BDCC

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“…Firstly, the manual captioning of images by humans is a time-consuming and costly process, rendering it impractical for handling a large volume of images. Secondly, each individual assigns different captions to an image based on their personal preferences and comprehension, thereby introducing subjective biases into the captioning process [10]. In the approach of content-based image retrieval, pictures are captioned with basic characteristics such as color and texture [11].…”

Section: Introductionmentioning

confidence: 99%

Image Captioning by Using Bidirectional LSTM Neural Network

Hoseini,

Notash

2024

Preprint

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Putting captions on images is an important phase in image processing and machine vision which divides an image into different regions. In the related captioning operation, similar attributes are extracted from each region and a single caption is opted for the title of the input for the relevant image. In the developed world, due to the considerable volume of images that have been available on the Internet, it is complicated to choose a suitable method for captioning images automatically and determining concepts correctly. In this study, a method based on a Bidirectional LSTM neural network with 14 layers is used for automatic captioning. The dataset is trained on ImageNet by MobileNet architecture. Due to the high volume of the data set consequently with the huge calculations that are required for captioning, MobileNet architecture has been applied to decrease the computational load and to boost the performance of the proposed method. in this study, the data set is flickr8k with a volume of data of more than 1.04 GB which was selected as one of the challenging data sets for captioning images through the Kaggle site. The proposed method is implemented with Python language by artificial intelligence package TensorFlow and Cross on Google Colab. The proposed Bidirectional LSTM neural network has been compared with five other models based on the LSTM neural network. So, the results of these comparisons have been evaluated for Precision, Accuracy, Recall, F score, and Loss Function. According to the hardware's limitations and the huge volume of calculations, the proposed model was able to show convincing performance compared to other models. The obtained results with an accuracy of 75.90% indicate the improvement of the accuracy as well as the reduction of the cost in the captioning process for the proposed model.

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