Content-Based Image Retrieval (CBIR) systems are currently a widely used solution for
image retrieval tasks with various applications. Despite the advances achieved, one of the …

To date, query performance prediction (QPP) in the context of content-based image retrieval
remains a largely unexplored task, especially in the query-by-example scenario, where the …

The goal of query performance prediction (QPP) is to automatically estimate the
effectiveness of a search result for any given query, without relevance judgements. Post …

The task of Query Performance Prediction (QPP) in Information Retrieval (IR) involves
predicting the relative effectiveness of a search system for a given input query. Supervised …

Image retrieval plays an increasingly important role in our daily lives. There are many factors
which affect the quality of image search results, including chosen search algorithms, ranking …

Image retrieval has achieved remarkable improvements with the rapid progress on visual
representation and indexing techniques. Given a query image, search engines are expected …

Query difficulty estimation predicts the performance of the search result of the given query. It
is a powerful tool for multimedia retrieval and receives increasing attention. It can guide the …

The main objective of Content-Based Image Retrieval (CBIR) systems is to retrieve a ranked
list containing the most similar images of a collection given a query image, by taking into …

Query difficulty estimation (QDE) attempts to automatically predict the performance of the
search results returned for a given query. QDE has long been of interest in text retrieval …

The query-performance prediction task aims at estimating the retrieval effectiveness of
queries without obtaining relevance feedback from users. Most of the recently proposed …