Corpus Construction and Semantic Analysis of Indonesian Image Description

Abstract

Understanding language grounded in visual content is a challenging problem that has raised interest in both the computer vision and natural language processing communities. Flickr30k, which is one of the corpora that have become a standard benchmark to study sentence-based image description, was initially limited to English descriptions, but it has been extended to German, French, and Czech. This paper describes our construction of an image description dataset in the Indonesian language. We translated English descriptions from the Flickr30K dataset into Indonesian with automatic machine translation and performed human validation for the portion of the result. We then constructed Indonesian image descriptions of 10k images by crowdsourcing without English descriptions or translations, and found semantic differences between translations and descriptions. We conclude that the cultural differences between the native speakers of English and Indonesian create different perceptions for constructing natural language expressions that describe an image.

Hybrid of Deep Learning and Word Embedding in Generating Captions: Image-Captioning Solution for Geological Rock Images

“…x i−1 ; I) [26][27][28][29][30][31][32][33][34][35][36]. Model image captioning has been researched by many using CNN block and language models, such as DenseNet and LSTM [9,17], CNN and LSTM [19,26,33,[37][38][39], inceptionV3 and RNN [14], and CNN and BERT [40,41]. One of the important parts of captioning is word embedding, which provides a vector feature value for each word.…”