Designing Novel Image Search Interfaces by Understanding Unique Characteristics and Usage

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 that users interact with search engines, and therefore affect existing methods of search evaluation. Although different evaluation metrics have been thoroughly studied in the general Web search environment, how those offline and online metrics reflect user satisfaction in the context of image search is an open question. To shed light on this, we conduct a laboratory user study that collects both explicit user satisfaction feedbacks as well as user behavior signals such as clicks. Based on the combination of both externally assessed topical relevance and image quality judgments, offline image search metrics can be better correlated with user satisfaction than merely using topical relevance. We also demonstrate that existing offline Web search metrics can be adapted to evaluate on a two-dimensional presentation for image search. With respect to online metrics, we find that those based on image click information significantly outperform offline metrics. To our knowledge, our work is the first to thoroughly establish the relationship between different measures and user satisfaction in image search.

Web-based image search engines differ from Web search engines greatly. The intents or goals behind human interactions with image search engines are different. In image search, users mainly search images instead of Web pages or online services. It is essential to know why people search for images because user satisfaction may vary as intent varies. Furthermore, image search engines show results differently. For example, grid-based placement is used in image search instead of the linear result list, so that users can browse result list both vertically and horizontally. Different user intents and system UIs lead to different user behavior. Thus, it is hard to apply standard user behavior models developed for general Web search to image search. To better understand user intent and behavior in image search scenarios, we plan to conduct the lab-based user study, field study and commercial search log analysis. We then propose user behavior models based on the observation from data analysis to improve the performance of Web image search engines.

Variation in Results of Three Biology‐Focused Search Engines: A Case Study Using North American Tree Species

General purpose Web search engines can be a valuable tool to locate high quality information and can complement the use of specialized systems, such as LexisNexis or WestLaw. The effective use of Web search engines differs somewhat from that of other information retrieval systems and requires an understanding of search engine design and procedures before effective use. We present an overview of search engines and user behavior in Web searching as an introduction to a discussion of the effectiveness of using some of the advanced searching techniques common in other searching environments in Web searches. We empirically test the effectiveness on query operators for Web searching. The use of operators such as AND, OR, and PHRASE was found to have little or no difference on the number of relevant results retrieved for three major search engines, America Online, Google, and Microsoft Search.

Searching the Web, Continued

Due to the dynamic nature of web and the complex architectures of modern commercial search engines, top results in major search engines can change dramatically over time. Our experimental data shows that, for all three major search engines (Google, Bing and Yahoo!), approximately 90% of queries have their top 10 results altered within a period of ten days. Although this instability is expected in some situations such as in news-related queries, it is problematic in general because it can dramatically affect retrieval performance measurements and negatively affect users' perception of search quality (for instance, when users cannot re-find a previously found document). In this work we present the first large scale study on the degree and nature of these changes. We introduce several types of query instability, and several metrics to quantify it.We then present a quantitative analysis using 12,600 queries collected from a commercial web search engine over several weeks. Our analysis shows that the results from all major search engines have similar levels of instability, and that many of these changes are temporary. We also identified classes of queries with clearly different instability profiles - for instance, navigational queries are considerably more stable than non-navigational, while longer queries are significantly less stable than shorter ones.

When we search information on the Web, we usually enter a query that best represents our information need, and then modify it as the search results are shown. Many thus have studied search queries to understand how users express and find their information need on the Web. For example, studies have shown that query length affects search performance (Belkin et al, 2003). Then, researchers have begun to examine image search queries on the Web. They analyzed search logs from general or commercial search engines or studied specific image database systems based on log analysis (Goodrum & Sprink, 2001; Jorgensen & Jorgensen, 2005; Goodrum, Bejune, & Siochi, 2003; Pu, 2005; Cunningham, Bainbridge, & Masoodian, 2004). While these studies provide basic understanding of image query formulation, further characteristics of user's queries in the interactive searching process have not been explored. This study thus builds on previous studies on image search queries by examining the patterns of query formulation and modification in visual information search. Specifically, first, it investigates the effects of tasks and other contextual factors on query length and query changes; and second, it compares the query length and changes with the user's satisfaction of search results. Participants Twenty-nine college students were recruited for data collection during the spring and summer semesters of 2008, mostly from the Department of Media Studies at the Catholic University of America (CUA). They included 22 from Media Studies and 7 from other fields taking courses in the department; 22 were females and 7 males; and an average age of 21 years. Background information collected were years of computer experience, hours per day using the Web, self-rated online searching expertise, and self-rated digital image searching experience. The seven-point Likert scale was used for self-rating variables. Each participant took part in three different search sessions with his or her own search topic at CUA with the researcher's laptop that had an Internet connection. The university's homepage was set as the default home for the Web browser. Each participant filled out a pre-search questionnaire to collect a search task, topic, self-rated topic familiarity using 7-Likert scale. The participants chose their own preferred browser between Internet Explorer and Firefox. The search session was recorded by Camtasia v5.0 as screen capture. After the session, a post-search questionnaire was given to the participants to assess the level of satisfaction and confidence of their searching using 7-Likert scale. There was no time limit to complete a search session. The participants were free to end a search session at any time. Search Tasks The participants brought their own task and topics to find images. There were three types of search tasks identified: academic task, work-related task, and personal interest (Table 1). Search queries were acquired from screen capture log files. A total of 979 queries were obtained as the entire string of terms submitted by the participants. Table 2 shows a distribution of search queries on a general search engine, a search engine's image search, and other specific sites. Findings showed that Google Images Search was the most frequent source for forming a search query. An average word per query was 3.07 during the first session, 2.95 during the second session, and 2.89 for the last session. This finding of short queries is similar to that of previous studies on Web image queries (Goodrum & Spink, 2001; Jorgensen & Jorgensen, 2005). Analysis of data is in progress. A poster will summarize detail findings and provide answers to the following questions: What are the patterns of query modification? Are there any differences of query length and number of query change based on the task and topic familiarities? Is there any relationship between query length and number of query change and users' satisfaction and confidence with search results? Do different stages of a user's particular task affect query length and number of query change? The author would like to thank OCLC/ALISE for their generous funding for this project.

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1) Why do people search for images in text-based Web image search systems? (2) How does image search behavior change with user intent? (3) Can we predict user intent effectively from interactions during the early stages of a search session? To this end, we conduct both a lab-based user study and a commercial search log analysis. We show that user intents in image search can be grouped into three classes: Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals different user behavior patterns under these three intents, such as first click time, query reformulation, dwell time and mouse movement on the result page. Based on user interaction features during the early stages of an image search session, that is, before mouse scroll, we develop an intent classifier that is able to achieve promising results for classifying intents into our three intent classes. Given that all features can be obtained online and unobtrusively, the predicted intents can provide guidance for choosing ranking methods immediately after scrolling.

Today search engines have become one of the most critical applications on the Web, driving many important online businesses that connect users to information. As the Web continues to grow its size with a variety of new data and penetrate into every aspect of our life, the need for developing a more intelligent search engine is increasing.The primary function of current Web search engines is essentially relevance ranking at the document level, an old paradigm in information retrieval for more than 25 years. To make web search more intelligent, Microsoft Research Asia is currently developing a set of technologies to enable searching at the object level. For example, when a user is looking for information about a researcher, the user is not interested in just retrieving a set of papers or documents in which this researcher's name appears, but more interested in finding insight and knowledge about this researcher as to what works he has done in different period of time, what important contributions he has brought to the research community, how influential he is, and his social networks, etc. Such kind of intelligence is not possible to obtain through current search engines.In this talk, I will present the technical problems that we need to address, including information extraction, object identification and integration, and object relationship mining and ranking. I will also show how we apply these technologies to build more advanced vertical search engines such as product search, academic search, and image search that provide users not only relevant search results but also intelligence and insights in the retrieved data.

BackgroundAcute pancreatitis is a prevalent condition in China. A plethora of information pertaining to acute pancreatitis is readily available on the Internet, including on major search engines and various short video applications. The objective of this study was to evaluate and compare the quality, content completeness, and accuracy of information related to acute pancreatitis on web pages of the four major search engines and short videos on the two major short video platforms.Materials and methodsA search was conducted on four major search engines (Bing, Baidu, Haosou, and Sougou) and two short video apps (TikTok and BiliBili) using the Chinese keyword “acute pancreatitis.” The sources can be divided into four categories: scientific resources, news/media reports, industrial/commercial profit organizations, and healthcare providers. The quality of the web pages and short videos was evaluated using the DISCERN instrument, the Global Quality Score (GQS), and the Journal of the American Medical Association (JAMA). In accordance with established guidelines and reviews of acute pancreatitis, two lists and scoring systems were devised for the evaluation of content comprehensiveness and accuracy.ResultsA total of 120 unique web pages and 120 unique videos were identified using four search engines and two short video applications, respectively. The most prevalent identity among those producing short videos was that of healthcare providers. With regard to the source of the web pages, the most prevalent category was that of industrial/commercial profit organizations. The median DISCERN total score, median GQS score, and median JAMA score were 26, 3, and 2, respectively. Web pages exhibited significantly higher ratings than short videos (p < 0.001, p = 0.003, and p < 0.001). The median overall content score for the web page was 8 (interquartile range [IQR] 4–13), and the median guideline-related score was 2 (IQR 0–4), indicating that the web page’s content was deficient in terms of completeness and accuracy. In comparison, the performance of the short video was found to be even more deficient.ConclusionIn China, most web pages pertaining to acute pancreatitis were produced by industrial/commercial profit organizations, but the quality of the information provided by these entities was found to be the lowest. The majority of short videos were created by healthcare providers, but the overall quality of these videos was found to be inadequate. In general, the quality of both web pages and short videos is suboptimal. Nevertheless, the quality of web pages was found to be superior to that of short videos. In terms of completeness and accuracy, both the web page and the video exhibit significant deficiencies that are cause for concern.

The online encyclopedia Wikipedia is a frequently referred- to source of information for Internet users. A series of 3 studies examined Wikipedia's coverage of psychology-related concepts, examined how accessible Wikipedia's psychology content is when using Internet search engines, and described how both first-year and senior undergraduates use Wikipedia. The results demonstrated that Wikipedia's coverage of psychological topics was comprehensive and prominently displayed on the major search engines. In addition, a majority of undergraduate students reported referring to Wikipedia for both personal and school-related activities; however, few students reported using Wikipedia as a formal reference in academic work.

Different types of search engines are used to search image and text contents. Two types of image search methods are available in the Internet. They are query keyword based model and content based image retrieval models. Text query strings are used in the textual image retrieval model. Content based image retrieval (CBIR) model uses the visual information of the images. Image search methods use the text annotation and image visual features. Google image search and Bing image search engines are used to fetch images from the web. Image query string is used to search image on Internet. One click query image selection method is used to submit user intention for image retrieval. Content based image re-ranking is performed with visual and textual similarity metrics. Adaptive Weight Schema is used for the similarity analysis. Feature weight learning algorithm is applied to estimate feature weights for the images and its category. Query is expanded with keyword and visual information. Rank boost framework algorithm is enhanced to rank images with photographic quality. Content similarity and visual quality factors are used for the re-ranking process. In this paper, we propose an image indexing and retrieval using speech annotations based on a predefined structured syntax. To improve the retrieval effectively, N-best lists for index generation is used .so, a query expansion technique is explored to enhance the query terms. All this process is automatic, without extra effort from the user. This is critically important in web-based image search engine for any commercial, where the user interface has to be extremely simple.

PurposeIt is important to measure the interaction between conversing in social media and searching on the web in order to understand the impact on electronic word-of-mouth marketing. The paper aims to discuss this issue.Design/methodology/approachThe authors research the relationship between social media conversing and web searching concerning brands on three major social soundtrack platforms (Instagram, Twitter, and Tumblr) and on a major web search engine (Google). The authors examine the effects of changes in both volume and attitude of conversing and volume of searching for two phases (Pre and Post) concerning brands in commercials aired during Super Bowl XLIX. The authors perform Granger causality testing and panel data regression analysis to determine the causal relationship between social media conversing and web searching.FindingsResults show that volume and attitude of social media conversing has a significant causality relationship to the volume of web searching. Each unit increase of volume on Twitter, Instagram, and Tumblr significantly increases Google search volume for the same brands by 4.7 times, 11.9 times, and 8.7 times, respectively. Each unit increase of attitude score on Twitter significantly increases web search volume 3.96 times, while for Tumblr, search volume significantly increases 0.95 times with each unit. Interestingly, search volume also has a significant causality relationship on the volume of social media postings.Originality/valueThis research seeks to understand the commercial impacts of the interaction among broadcast advertising, social media conversing, and web searching for which there is limited prior work, especially in the context of a major media event.

This article studies a novel problem in image search. Given a text query and the image ranking list returned by an image search system, we propose an approach to automatically predict the search performance. We demonstrate that, in order to estimate the mathematical expectations of Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG), we only need to predict the relevance probability of each image. We accomplish the task with a query-adaptive graph-based learning based on the images’ ranking order and visual content. We validate our approach with a large-scale dataset that contains the image search results of 1,165 queries from 4 popular image search engines. Empirical studies demonstrate that our approach is able to generate predictions that are highly correlated with the real search performance. Based on the proposed image search performance prediction scheme, we introduce three applications: image metasearch, multilingual image search, and Boolean image search. Comprehensive experiments are conducted to validate our approach.

Image search has been widely deployed in many applications for the rich content that images contain. In the era of big data, image search engines have to be hosted in data centers. As a viable solution, outsourcing the image search to public clouds is an economic choice for many small organizations. However, as many images contain sensitive information, e.g., healthcare information and personal faces/locations, directly outsourcing image search services to public clouds obviously raises privacy concerns. With this observation, several attempts are made towards secure image search over encrypted dataset, but they are limited by either search accuracy or search efficiency. In this paper, we propose a lightweight secure image search scheme over encrypted data, namely SEISA. Compared with image search techniques over plaintexts, SEISA only increases about 9% search cost and sacrifices about 3% on search accuracy. SEISA also efficiently supports search access control by employing a novel polynomial based design, which enables data owners to define who can search a specific image. Furthermore, we design a secure k-means outsourcing algorithm that significantly saves the data owner's cost. To demonstrate SEISA's performance, we implement a prototype of SEISA on Amazon EC2 cloud over a dataset with 10 million images.