Search Results

Following the success of Cranfield-like evaluation approaches to evaluation in web search, web image search has also been evaluated with absolute judgments of (graded) relevance. However, recent research has found that collecting absolute relevance ...

In web image search, items users search for are images instead of Web pages or online services. Web image search constitutes a very important part of web search. Re-ranking is a trusted technique to improve retrieval effectiveness in web search. ...

Comparing to general Web search engines, image search engines present search results differently, with two-dimensional visual image panel for users to scroll and browse quickly. These differences in result presentation can significantly impact the way ...

User interaction behavior is a valuable source of implicit relevance feedback. In Web image search a different type of search result presentation is used than in general Web search, which leads to different interaction mechanisms and user behavior. For ...

User interfaces for web image search engine results differ significantly from interfaces for traditional (text) web search results, supporting a richer interaction. In particular, users can see an enlarged image preview by hovering over a result image, ...

Existing web image search engines are mainly designed to optimize topical relevance. However, according to our user study, attractiveness is becoming a more and more important factor for web image search engines to satisfy users' search intentions. ...

Many user studies in Web information searching have found the significant effect of task types on search strategies. However, little attention was given to Web image searching strategies, especially the query reformulation activity despite that this is ...

We explore the application of a graph representation to model similarity relationships that exist among images found on the Web. The resulting similarity-induced graph allows us to model in a unified way different types of content-based similarities, as ...

Images are major source of Web content. Image annotation is an important issue which is adopted to retrieve images from large image collections based on the keyword annotations of images, which access a large image data-base with textual queries. With ...

This demo presents a web image search engine via learning semantics of query image. Unlike traditional CBIR systems which search images according to visual similarities, our system implements an extended CBIR (ExCBIR) which returns both visually and ...

This paper proposes a novel semantic-aware distance metric for images by mining multimedia data on the Internet, in particular, web images and their associated tags. As well known, a proper distance metric between images is a key ingredient in many ...

In this paper, we propose an approach to cluster web image search results based on color (CbC). We index images into color histogram descriptors and improved-color distribution entropy descriptors (I-CDE), and cluster them by the similarity of color ...

Image search has recently gained more and more attention for various applications. To capture users' intensions and to bridge the gap between the low level visual features and the high level semantics, a dozen of interactive reranking (IR) or relevance ...

Fusion of visual content with textual information is an effective way for both content-based and keyword-based image retrieval. However, the performance of visual & textual fusion is affected greatly by the data noise and redundancy in both text (such ...

Web image search is difficult in part because a handful of keywords are generally insufficient for characterizing the visual properties of an image. Popular engines have begun to provide tags based on simple characteristics of images (such as tags for ...

Automatic image annotation is fundamental for effective image browsing and search. With the increasing size of image collections such as web images, it is infeasible to manually label large numbers of images. Meanwhile, the textual information contained ...

In this demo, we present IGroup, a Web image search engine that organizes the search results into semantic clusters. Different from all existing Web image search results clustering algorithms that only cluster the top few images using visual or textual ...

In this paper, we propose, IGroup, an efficient and effective algorithm that organizes Web image search results into clusters. IGroup is different from all existing Web image search results clustering algorithms that only cluster the top few images ...

Nowadays, mobile phones with the digital camera are getting more and more popular. With necessary technologies, they are possible to become a powerful tool to search the Web on the go. Most Web search engines only support text queries. Therefore, users ...

We propose a new method for automated large scale gathering of Web images relevant to specified concepts. Our main goal is to build a knowledge base associated with as many concepts as possible for large scale object recognition studies. A second goal ...