Quality and Efficiency of Manual Annotation: Pre-annotation Bias
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This paper presents an analysis of annotation using an automatic pre-annotation for a mid-level annotation complexity task -- dependency syntax annotation. It compares the annotation efforts made by annotators using a pre-annotated version (with a high-accuracy parser) and those made by fully manual annotation. The aim of the experiment is to judge the final annotation quality when pre-annotation is used. In addition, it evaluates the effect of automatic linguistically-based (rule-formulated) checks and another annotation on the same data available to the annotators, and their influence on annotation quality and efficiency. The experiment confirmed that the pre-annotation is an efficient tool for faster manual syntactic annotation which increases the consistency of the resulting annotation without reducing its quality.Keywords:
Temporal annotation
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In this paper, we propose an example-based method for automatic image annotation. The advantage of this method is that it can determine the image annotation using former annotation experiences, which overcomes the shortcomings of manual-annotation. Also, the method can be extended as semi-automatic annotation, which provides users with a simple and convenient interface for annotation.
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Nowadays, increasing demand on image annotation requirement increases the number of automatic image annotation systems. However, performances of current annotation methods are far from practical usage. Common problem of current methods is the gap between semantic words and low level visual descriptors. Because of semantic gap, annotation results of these methods contain irrelevant noisy words. To give more relevant results, refinement methods should be applied. In this work, we represent a novel refinement approach for image annotation problem. Proposed system produces candidate annotations by combining relation between words and raw annotation. Optimal one of the produced annotations is used for annotation.
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This paper investigates new approaches to improve the efficiency of manual image annotation and help users to produce better annotation results in a given amount of time. Although important in practice, this issue has rarely been studied in a quantitative way before. To achieve this, we first propose two time models to analyze the annotation process for two popular manual annotation approaches, i.e., tagging and browsing. The complementary properties of these approaches have inspired us to merge them to develop a hybrid annotation algorithms called frequency-based annotation. Our experiments on large-scale multimedia collections have shown that the proposed algorithm can achieve an up to 40% annotation time reduction compared with the baseline methods. In other words, it can produce considerably better results using the same annotation time.
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In this paper, we present a novel method for image annotation and retrieval on mobile device by using contextual information. Image annotation is an effective way for content based image retrieval. However, manual annotation is an expensive and time consuming work, especially for mobile device. Here, we propose a semi-automatic way to give image annotation on mobile device. There are rich contexts for a mobile device, such as photo captured context, personal context and social network context. We synthesize these contexts and get useful semantic content of the photo. The synthesized results are treated as annotation suggestions. Our annotations include time, location, event, persons etc. We have implemented the annotation method in a prototype. The results show the method is simple and efficient.
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The article provides an analysis of image annotation process for artificialintelligence models within modern detection systems learning using modern tools for annotation. Software application requirements and parameters list has been formed for image annotation, which are sufficiently consistent with the annotation process. Provided charts displays key parameters for image annotation process in modern applications. Mass factor approach role importance reviewed in accordance with annotation task solving in modern recognition systems. Yoloanno application has been developed, whichincorporates all requirements for an annotation process: functional and timing, - and provides tools to solve the task, what was proven during the experiments. Obtained results could be used for image annotation task practical solution, as well as provided approaches could be used for new image annotation applications creation. Ref. 8, pic. 4
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Although important in practice, manual image annotation and retrieval has rarely been studied by means of formal modeling methods. In this chapter, the authors propose a set of formal models to characterize the annotation times for two commonly-used manual annotation approaches, that is, tagging and browsing. Based on the complementary properties of these models, the authors design new hybrid approaches, called frequency-based annotation and learning-based annotation, to improve the efficiency of manual image annotation as well as retrieval. Both our simulation and experimental results show that the proposed algorithms can achieve up to a 50% reduction in annotation time over baseline methods for manual image annotation, and produce significantly better annotation and retrieval results in the same amount of time.
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A multimedia system for semi-automated image annotation, Show&Tell combines advances in speech recognition, natural language processing and image understanding. Show&Tell differs from map annotation systems and has tremendous implications for situations where visual data must be co-referenced with text descriptions, such as medical image annotation and consumer photo annotation.
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In content-based image retrieval,annotations of image can not only reduce the gap between high-grade semantics and low-grade visual content,but is also convenient for retrieval.As we all know,manual-annotation is time-consuming,strength-consuming,and the annotation results may be subjectively different,while the automatic image annotation can transform the visual features into annotations.This benefits users a lot.In this paper,we propose an instance-based method for automatic image annotation.The advantage of this method is that it can determine the image annotation according to previous annotation experiences,which overcomes the shortcomings of manual-annotation.Moreover,the method can be extended to semi-automatic annotation,which provides users with a simple and convenient interface for annotation.
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In this paper, we propose a novel approach of image annotation byconstructing a hierarchical mapping between low-level visualfeatures and text features utilizing the relations within and acrossboth visual features and text features. Moreover, we propose a novelannotation strategy that maximizes both the accuracy and thediversity of the generated annotation by generalizing or specifyingthe annotation in the corresponding annotation hierarchy.Experiments with 4500 scientific images from Royal Society ofChemistry journals show that the proposed annotation approachproduces satisfactory results at different levels of annotations.
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