Comparing the Language Used in Flickr, General Web Pages, Yahoo Images and Wikipedia (original) (raw)
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Here we present a study comparing vocabulary from two different language models: the language used by people to describe their pictures on Flickr, and unrestricted language found on the Web. We examine these two language models through the lens of the semantically typed vocabulary in the General Inquirer lexicon. Developed in the 1960s for computerized content analysis of text, it provides a number of semantic categories for each word it contains. We compare the relative frequencies of usage of these categories, and usage of individual word within these categories, in Flickr tags versus the Web, using the weirdness metric from research in special languages. The distribution of words used in each independent set, image annotations and Web text, is found to be different as expected, and we describe the differences. We show, for example, that noun Flickr tags are much more preferred over adjectives and verbs, more than the Web would lead us to expect. Names for animals, humans, and weddings also appear more frequently in Flickr tag than on the Web. Exploring these two language models helps us to better understand the vocabulary specific to images, which should ultimately be helpful in determining vocabulary for automated image annotation.
Image annotation: the effects of content, lexicon and annotation method
International Journal of Multimedia Information Retrieval, 2020
Image annotation is the process of assigning metadata to images, allowing effective retrieval by text-based search techniques. Despite the lots of efforts in automatic multimedia analysis, automatic semantic annotation of multimedia is still inefficient due to the problems in modelling high level semantic terms. In this paper we examine the factors affecting the quality of annotations collected through crowdsourcing platforms. An image dataset was manually annotated utilizing: (i) a vocabulary consists of pre-selected set of keywords, (ii) an hierarchical vocabulary, and (iii) free keywords. The results show that the annotation quality is affected by the image content itself and the used lexicon. As we expected while annotation using the hierarchical vocabulary is more representative, the use of free keywords leads to increased invalid annotation. Finally it is shown that images requiring annotations that are not directly related to their content (i.e. annotation using abstract concepts), lead to accrue annotator inconsistency revealing in that way the difficulty in annotating such kind of images is not limited to automatic annotation, but it is generic problem of annotation.
The Assignment of Tags to Images in Internet: Language Skill Evaluation for Tag Recommendation
Users who tag images in Internet using the English language, an be native in that language or non native. Also, they can have different levels of tagging skills, in semantic terms (richness of vocabulary) and syntactic terms (errors incurred while defining the tags). If we can identify the ‘best’ taggers, we can use their work to help taggers whose skills are not so good. In this paper we present a study carried out for native and non native English language taggers, with the objective of providing user support depending on the detected language skills and characteristics of the user. In order to do this, we study the different types of syntactic errors that taggers commit, and analyze different semantic factors related to objective and subjective tagging, given that our hypothesis is that the latter is in general more difficult. We find that the syntactic and semantic factors together, allow us to profile users in terms of their skill level. This would allow us to keep the tag sessions of the best users and provide their tags to users who have a lower skill level.
Image Tagging in the Spanish Language in Internet-A User Study and Data Analysis
Web Congress, 2009. LE- …, 2009
Authors: David F. Nettleton, Mari-Carmen Marcos, Bartolomé Mesa-Lao. In LA-WEB '09 Proc. 2009 Latin American Web Congress, pp. 120-127. IEEE Computer Society Washington, DC, USA. Users who tag images in Internet using the Spanish language can be from different Spanish speaking countries in the world. Different countries with different cultures, variations in vocabulary and forms of expression, which can influence in their choice of tags while tagging images. Also, they can have different levels of tagging skills, in semantic terms (diversity of vocabulary) and syntactic terms (errors incurred while defining the tags). In this paper we present a study carried out for natives of different Spanish speaking countries, with the objective of providing user support depending on the detected language skills and characteristics of the user. Using the syntactic and semantic factors we can profile users in terms of their skill level and other characteristics, and then we can use these profiles to offer customized support for image tagging in the Spanish language.
"Image tagging in Internet is becoming a crucial aspect in the search activity of many users all over the world, as online content evolves from being mainly text based, to being multi-media based (text, images, sound, …). In this paper we present a study carried out for native and non native English language taggers, with the objective of providing user support depending on the detected language skills and characteristics of the user. In order to do this, we analyze the differences between how users tag objectively (using what we call ‘see’ type tags) and subjectively (by what we call ‘evoke’ type tags). We study the data using bivariate correlation, visual inspection and rule induction. We find that the objective/subjective factors are discriminative for native/non native users and can be used to create a data model. This information can be utilized to help and support the user during the tagging process."
A Text-Based Approach to the ImageCLEF 2010 Photo Annotation Task
The challenges of searching the increasingly large collections of digital images which are appearing in many places mean that automated annotation of images is becoming an important task. We describe our participation in the ImageCLEF 2010 Visual Concept Detection and Annotation Task. Our approach used only the textual features (Flickr user tags and EXIF information) to perform the automatic annotation. Our approach was to explore the use of different techniques to improve the results of textual annotation. We identify the drawbacks of our approach and how these might be addressed and optimized in further work.
A Framework For Annotating Images and its Respective Tags.
International Journal of Engineering Sciences & Research Technology, 2014
The vast resource of pictures available on the web and the fact that many of them naturally co-occur with topically related documents and are captioned we focus on the task of automatically generating captions for images, here the model learns to create captions from a database of news articles, and the pictures embedded in them, and their captions, and consists of two stages. Content selection identifies what the image and accompanying article are about, whereas surface realization determines how to verbalize the chosen content. We approximate content selection with a probabilistic image annotation model that suggests keywords for an image. In the Proposed system extensive features are extracted from the database images and stored in the feature library. The extensive features set is comprised of shape features along with the color, texture and the contour let features, which are utilized in this work. When a query image is given, the features are extracted in the similar fashion. Subsequently, GA-based similarity measure is performed between the query image features and the database image features.
2007
We describe a series of studies aimed at identifying specifications for a text extraction module of an image indexer’s toolkit. The materials used in the studies consist of images paired with paragraph sequences that describe the images. We administered a pilot survey to visual resource center professionals at three universities to determine what types of paragraphs would be preferred for metadata selection. Respondents generally showed a strong preference for one of two paragraphs they were presented with, indicating that not all paragraphs that describe images are seen as good sources of metadata. We developed a set of semantic category labels to assign to spans of text in order to distinguish between different types of information about the images, thus to classify metadata contexts. Human agreement on metadata is notoriously variable. In order to maximize agreement, we conducted four human labeling experiments using the seven semantic category labels we developed. A subset of ou...
Text analysis for automatic image annotation
2007
We present a novel approach to automatically annotate images using associated text. We detect and classify all entities (persons and objects) in the text after which we determine the salience (the importance of an entity in a text) and visualness (the extent to which an entity can be perceived visually) of these entities. We combine these measures to compute the probability that an entity is present in the image. The suitability of our approach was successfully tested on 100 image-text pairs of Yahoo! News.
Explorations in automatic image annotation using textual features
Proceedings of the Third Linguistic …, 2009
In this paper, we report our work on automatic image annotation by combining several textual features drawn from the text surrounding the image. Evaluation of our system is performed on a dataset of images and texts collected from the web. We report our findings through comparative evaluation with two gold standard collections of manual annotations on the same dataset.