Ornament Image Retrieval Using Multimodal Fusion

Scalable Image Retrieval with Multimodal Fusion

As the number of images grows rapidly on the Internet, the scalability of image retrieval systems becomes a significant issue. In this paper, we propose two distributed clustering algorithms to scale up the bag-ofvisual-words model on millions of images and billions of visual features by leveraging distributed systems. We also introduce a multimodal fusion model to utilize textual data to impro...

Multimodal medical image fusion based on Yager’s intuitionistic fuzzy sets

The objective of image fusion for medical images is to combine multiple images obtained from various sources into a single image suitable for better diagnosis. Most of the state-of-the-art image fusing technique is based on nonfuzzy sets, and the fused image so obtained lags with complementary information. Intuitionistic fuzzy sets (IFS) are determined to be more suitable for civilian, and medi...

Fusion vs. Two-Stage for Multimodal Retrieval

We compare two methods for retrieval from multimodal collections. The first is a score-based fusion of results, retrieved visually and textually. The second is a two-stage method that visually re-ranks the top-K results textually retrieved. We discuss their underlying hypotheses and practical limitations, and contact a comparative evaluation on a standardized snapshot of Wikipedia. Both methods...

Image Fusion For Medical Image Retrieval

Semiautomatic Image Retrieval Using the High Level Semantic Labels

Content-based image retrieval and text-based image retrieval are two fundamental approaches in the field of image retrieval. The challenges related to each of these approaches, guide the researchers to use combining approaches and semi-automatic retrieval using the user interaction in the retrieval cycle. Hence, in this paper, an image retrieval system is introduced that provided two kind of qu...