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Similarity Models for Content-based Video Retrieval
Veselý, Patrik ; Peška, Ladislav (advisor) ; Sixtová, Ivana (referee)
Multimedia retrieval is increasingly important with the skyrocketing multimedia vol- umes produced every day. Therefore many image and video retrieval tools are being developed utilising visual similarity modelling algorithms for similar image retrieval or various visualisations. As such, the quality of the similarity modelling is crucial for these tools. This thesis explores diverse similarity models, their agreement with human percep- tion of similarity and possible improvements of these models. The examined similarity models consisted of colour-based, SIFT-based, and DNN-based models. For the purpose of model evaluation, a user study was conducted to create a dataset of relative image similarity comprising both generic images as well as two compact domains. In this study, the participants were asked to state which of the candidate images was more similar to the query image. The collected data showed the superiority of DNN-based models compared to other evaluated variants. Nonetheless, all similarity models performed significantly better than a random guess. In order to further enhance the performance of the simi- larity models, we fine-tuned the best-performing model (W2VV++) with the collected dataset and achieved significant improvement in some areas. 1
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Known-item search with relevance to SOM feedback
Veselý, Patrik ; Lokoč, Jakub (advisor) ; Vomlelová, Marta (referee)
Multimedia searching is usually realized by means of text search, where a large dataset is sorted with respect to a relevance to a given text query. However, if users search for just one scene or image, a sequential browsing of a larger result set is often necessary, without a guarantee that the object is found in a reasonable time. This work focuses on methods relying on relevance feedback for more effective searching in a large collection of one million images. Several relevance update and display selection approaches are compared using simulations of relevance feedback. Our experiments reveal that the investigated models are a benefit to modern multimedia search engines. 1
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