Metaheuristics Optimization with Deep Learning Enabled Automated Image Captioning System

Abstract

Image captioning is a popular topic in the domains of computer vision and natural language processing (NLP). Recent advancements in deep learning (DL) models have enabled the improvement of the overall performance of the image captioning approach. This study develops a metaheuristic optimization with a deep learning-enabled automated image captioning technique (MODLE-AICT). The proposed MODLE-AICT model focuses on the generation of effective captions to the input images by using two processes involving encoding unit and decoding unit. Initially, at the encoding part, the salp swarm algorithm (SSA), with a HybridNet model, is utilized to generate effectual input image representation using fixed-length vectors, showing the novelty of the work. Moreover, the decoding part includes a bidirectional gated recurrent unit (BiGRU) model used to generate descriptive sentences. The inclusion of an SSA-based hyperparameter optimizer helps in attaining effectual performance. For inspecting the enhanced performance of the MODLE-AICT model, a series of simulations were carried out, and the results are examined under several aspects. The experimental values suggested the betterment of the MODLE-AICT model over recent approaches.

Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System for Visually Impaired People

“…The Image captioning outcomes of the RDOAI-ICS model on the Flickr8K dataset is given in Table 1 and Fig. 4 [22]. The experimental outcomes revealed that the RDOAI-ICS model had outperformed other Image captioning approaches under all measures.…”

“…Result analysis of RDOAI-ICS approach under Flickr8K datasetA comparative analysis of the RDOAI-ICS model on the Flickr8K dataset is given in Table2and Fig. 5[22]. The experimental outcomes displayed by the RDOAI-ICS algorithm have exhibited other Image captioning techniques in all measures.…”

Lighting Search Algorithm With Convolutional Neural Network-Based Image Captioning System for Natural Language Processing

“…3 illustrates some sample images. A set of brief comparative analyses is made with recent methods, namely hard-attention, Neural Image Caption (NIC) [36], softattention [37], hard attention [38], Spatial and Channel-wise Attention with CNN VGG (SCA-CNN-VGG) [39], and CNN [40] methods. In this study, three parameters were employed for investigational validation such as CIDEr, BLEU, and Meter.…”

“…IMAGE CAPTIONING RESULT OF LSAHCNN-ICS TECHNIQUE WITH OTHERAPPROACHES UNDER FLICKR30K DATASET[36][37][38][39][40] …”

“…IMAGE CAPTIONING RESULT OF LSAHCNN-ICS TECHNIQUE WITH OTHER APPROACHES UNDER THE MSCOCO DATASET[36][37][38][39][40] Fig.8shows the 𝑇𝑅 𝑎𝑐𝑐 and 𝑉𝐿 𝑎𝑐𝑐 achieved by the LSAHCNN-ICS algorithm at the MSCOCO database. The achieved result states that the LSAHCNN-ICS technique can be realized improved values of 𝑇𝑅 𝑎𝑐𝑐 and 𝑉𝐿 𝑎𝑐𝑐 .…”