Observations on Using Type-2 Fuzzy Logic for Reducing Semantic Gap in Content–Based Image Retrieval System

Darwish, Saad M.; Ali, Raad A.

doi:10.7763/ijcte.2015.v7.921

Cited by 2 publications

(1 citation statement)

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“…However, the unidirectional nature of the approach restricted its applicability to image-to-text tasks. Saad M.Darwith et al [2] investigate the use of Type-2 Fuzzy Logic as a means to bridge the semantic gap in Content-Based Image Retrieval (CBIR) systems. Their findings highlight the potential of this approach in enhancing image retrieval accuracy by addressing inherent uncertainties in semantic interpretations.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation

Che,

Lin,

Zhao

et al. 2023

Proceedings of the 2023 6th International Conference on Big Data Technologies

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The process of transforming input images into corresponding textual explanations stands as a crucial and complex endeavor within the domains of computer vision and natural language processing. In this paper, we propose an innovative ensemble approach that harnesses the capabilities of Contrastive Language-Image Pretraining (CLIP) models. Our ensemble framework encompasses two significant variations of the CLIP model, each meticulously designed to cater to specific nuances within the image-to-text transformation landscape. The first model introduces an elaborated architecture, featuring multiple layers with distinct learning rates, thereby amplifying its adeptness in capturing intricate relationships between images and text. The second model strategically exploits CLIP's inherent zero-shot learning potential to generate image-text embeddings, subsequently harnessed by a K-Nearest Neighbors (KNN) model. Through this KNN-based paradigm, the model facilitates image-to-text transformation by identifying closely related embeddings within the embedding space. Notably, our ensemble approach is rigorously evaluated, employing the cosine similarity metric to gauge the alignment between model-generated embeddings and ground truth representations. Comparative experiments vividly highlight the superiority of our ensemble strategy over standalone CLIP models. This study not only advances the state-of-the-art in image-to-text transformation but also accentuates the promising trajectory of ensemble learning in effectively addressing intricate multimodal tasks.

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Section: Related Workmentioning

confidence: 99%