Li A, Sun J, Ng JY, Yu R, Morariu VI, Davis LS (2017) Generating holistic 3D scene abstractions for text-based image retrieval. In: 2018 international conference on computing, mathematics and engineering technologies (iCoMET), pp 1–6 Nazir A, Ashraf R, Hamdani T, Ali N (2018) Content based image retrieval system by using HSV color histogram, discrete wavelet transform and edge histogram descriptor. Zhu L, Shen J, Xie L, Cheng Z (2017) Unsupervised visual hashing with semantic assistant for content-based image retrieval. Tyagi V (2017) Content-based image retrieval techniques: a review. 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 a generic problem of annotation. ![]() As we expected while annotation using the hierarchical vocabulary is more representative, the use of free keywords leads to increased invalid annotation. The results show that the annotation quality is affected by the image content itself and the used lexicon. ![]() An image dataset was manually annotated utilizing: (1) a vocabulary consists of preselected set of keywords, (2) an hierarchical vocabulary and (3) free keywords. In this paper, we examine the factors affecting the quality of annotations collected through crowdsourcing platforms. Despite the lots of efforts in automatic multimedia analysis, automatic semantic annotation of multimedia is still inefficient due to the problems in modeling high-level semantic terms. Image annotation is the process of assigning metadata to images, allowing effective retrieval by text-based search techniques.
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