When video search goes wrong

Building a video search engine on the Web is a very challenging problem. Compared with web page search, video search has its unique characteristics (such as high volume of data for each video, existence of multi-modal information including meta-data, visual content, audio, closed caption, etc). In this paper, we investigate some promising approaches to boosting the search relevance of a large scale video search engine on the Web. The contribution of our work is three-fold. (1) We developed a specialized video categorization framework which combines multiple classifiers based on different modalities. (2) By learning users' querying history and clicking log, we proposed an automatic query profile generation technique and applied the profile to query categorization. (3) A highly scalable system was developed, which integrates the online query categorization and offline video categorization. Naive Bayes with mixture of multinomials, Maximum Entropy, and Support Vector Machine categorization methods and the profile learn-ing technique were evaluated on a large scale set of video data on the Web. The evaluation of the developed system and user study has indicated that the joint categorization of queries and video data boosts the video search relevance and user search experience. The high efficiency of our approaches is also demonstrated by the good responsiveness of the system for the video search engine on the Web.

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.

With the widespread use of online video application, the amount of online video clips becomes huge. Web video search engines can help users to locate video clips they are interested in. However, most video search engines return similar or near-duplicate videos together in the result lists, which is inconvenient for users to browse. This paper proposes a novel approach to cluster similar web searched videos based on video visual similarities mining. The visual information is extracted for each video clip at first, then the video clips are clustered according to the pair-wise similarities among them. To evaluate the effectiveness of the proposed method, experiments are conducted on YouTube video search results.

Session search is an Information Retrieval (IR) task which handles a series of queries issued for a search task. In this paper, we propose a novel reinforcement learning style information retrieval framework and develop a new feedback learning algorithm to model user feedback, including clicks and query reformulations, as reinforcement signals and to generate rewards in the RL framework. From a new perspective, we view session search as a cooperative game played between two agents, the user and the search engine. We study the communications between the two agents; they always exchange opinions on whether the current stage of search is relevant and whether we should explore now. The algorithm infers user feedback models by an EM algorithm from the query logs. We compare to several state-of-the-art session search algorithms and evaluate our algorithm on the most recent TREC 2012 to 2014 Session Tracks. The experimental results demonstrates that our approach is highly effective for improving session search accuracy.

We investigate user requirements regarding the interface design for a semantic multimedia search and retrieval based on a prototypical implementation of a search engine for multimedia content on the web. Thus, unlike existing image search engines and video search engines, we are interested in true multimedia content combining different media assets into multimedia documents like PowerPoint presentations and Flash files. In a user study with 20 participants, we conducted a formative evaluation based on the think-aloud method and semi-structured interviews in order to obtain requirements to a future web search engine for multimedia content. The interviews are complemented by a paper-and-pencil questionnaire to obtain quantitative information and present mockups demonstrating the user interface of a future multimedia search and retrieval engine.

This article discusses a multiple search session model of end-users' interaction with information retrieval systems based on results from an exploratory study investigating end-users' search sessions over time with online public access catalogs (OPAC) or CD-ROM databases at different stages of their information seeking related to a current research project. Interviews were conducted with 200 academic end-users to investigate the occurrence of multiple search sessions. Results show that at the time of the interview, 57% of end-users had conducted multiple search sessions during their research project and 86% of end-users conducted their first search session at the beginning stage of their information-seeking process. Forty-nine percent of end-users had conducted between 1 and 6 search sessions and 8% more than 6 search sessions. Seventy percent of multiple search session end-users' had modified their search terms since their first search session. The implications of the findings for end-user training, information retrieval systems design, and further research are discussed. © 1996 John Wiley & Sons, Inc.


 
 
 Search engines are used in the web as a tool for information retrieval. Web Server is a large warehouse of heterogeneous and unstructured data so that to filter out relevant information from people, a search engine is needed. Search engines usually consist of page repositories, indexing modules, query modules and ranking modules. Search engines do not work alone, besides that there is a web browser that supports the work of this search engine to be more optimal. A browser is software that is run on a user's computer (user) that displays web documents or information taken from a web server [1]. A browser is the type of intermediary the user uses most often. This paper aims to analyze three search engines namely Google, Yahoo, and Bing based on existing features. These features include web search, image search, video search, news search, route search, book search, change search settings, display number of views, shopping, language translator. Google stands as the best search engine among all search engines, which works using the Page Rank algorithm. Page Rank is a numerical value that determines the importance of a web page by calculating the number of backlinks.
 
 

The increased availability of large amounts of data about user search behaviour in search engines has triggered a lot of research in recent years. This includes developing machine learning methods to build knowledge structures that could be exploited for a number of tasks such as query recommendation. Query flow graphs are a successful example of these structures, they are generated from the sequence of queries typed in by a user in a search session. In this paper we propose to modify the query flow graph by incorporating clickthrough information from the search logs. Click information provides evidence of the success or failure of the search journey and therefore can be used to enrich the query flow graph to make it more accurate and useful for query recommendation. We propose a method of adjusting the weights on the edges of the query flow graph by incorporating the number of clicked documents after submitting a query.We explore a number of weighting functions for the graph edges using click information. Applying an automated evaluation framework to assess query recommendations allows us to perform automatic and reproducible evaluation experiments. We demonstrate how our modified query flow graph outperforms the standard query flow graph. The experiments are conducted on the search logs of an academic organisation’s search engine and validated in a second experiment on the log files of another Web site.KeywordsSearch Log AnalysisQuery SuggestionsAutomatic Evaluation

The behaviour of the searcher when using the search engine especially during the query formulation is crucial. Search engines capture users’ activities in the search log, which is stored at the search engine server. Due to the difficulty of obtaining this search log, this paper proposed and develops an interface framework to interface a Google search engine. This interface will capture users’ queries before redirect them to Google. The analysis of the search log will show that users are utilizing different types of queries. These queries are then classified as breadth and depth search query. To cite this document: Fadhilah Mat Yamin and T. Ramayah, Interfacing Google search engine to capture user web search behavior, International Journal of Electronic Commerce Studies, Vol.4, No.1, pp.47-62, 2013. Permanent link to this document: http://dx.doi.org/10.7903/ijecs.1111

The dominant existing routing strategies employed in peer-to-peer(P2P) based information retrieval(IR) systems are similarity-based approaches. In these approaches, agents depend on the content similarity between incoming queries and their direct neighboring agents to direct the distributed search sessions. However, such a heuristic is myopic in that the neighboring agents may not be connected to more relevant agents. In this paper, an online reinforcement-learning based approach is developed to take advantage of the dynamic run-time characteristics of P2P IR systems as represented by information about past search sessions. Specifically, agents maintain estimates on the downstream agents' abilities to provide relevant documents for incoming queries. These estimates are updated gradually by learning from the feedback information returned from previous search sessions. Based on this information, the agents derive corresponding routing policies. Thereafter, these agents route the queries based on the learned policies and update the estimates based on the new routing policies. Experimental results demonstrate that the learning algorithm improves considerably the routing performance on two test collection sets that have been used in a variety of distributed IR studies.

Semantic search in video is a novel and challenging problem in information and multimedia retrieval. Existing solutions are mainly limited to text matching, in which the query words are matched against the textual metadata generated by users. This paper presents a state-of-the-art system for event search without any textual metadata or example videos. The system relies on substantial video content understanding and allows for semantic search over a large collection of videos. The novelty and practicality is demonstrated by the evaluation in NIST TRECVID 2014, where the proposed system achieves the best performance. We share our observations and lessons in building such a state-of-the-art system, which may be instrumental in guiding the design of the future system for semantic search in video.

An exploration of risk information search via a search engine: Queries and clicks in healthcare and information security

Children use popular web search tools, which are generally designed for adult users. Because children have different developmental needs than adults, these tools may not always adequately support their search for information. Moreover, even though search tools offer support to help in query formulation, these too are aimed at adults and may hinder children rather than help them. This calls for the examination of existing technologies in this area, to better understand what remains to be done when it comes to facilitating query-formulation tasks for young users. In this paper, we investigate interaction elements of query formulation--including query suggestion algorithms--for children. The primary goals of our research efforts are to: (i) examine existing plug-ins and interfaces that explicitly aid children's query formulation; (ii) investigate children's interactions with suggestions offered by a general-purpose query suggestion strategy vs. a counterpart designed with children in mind; and (iii) identify, via participatory design sessions, their preferences when it comes to tools / strategies that can help children find information and guide them through the query formulation process. Our analysis shows that existing tools do not meet children's needs and expectations; the outcomes of our work can guide researchers and developers as they implement query formulation strategies for children.

Search engine users issue queries to formulate their information need and gain useful insights. However, it is challenging for search engines to understand different users’ search type intents and return appropriate results. Simulating user search behaviour allows information retrieval systems (IR) to parameterise the a-priori distribution of search types using different back-end configurations and user interface variants to improve the retrieval functionality. In this paper, we propose a formal Markov approach in which we utilise the context discovery process to model user-type specific behaviour by capturing the user’s query change in a search session. Contextual Markov models have been used in the past to improve the prediction of user intentions, we investigate here their efficiency in simulating user-type specific interactions. Additionally, we provide an empirical and classification-based evaluation that can be used in simulation assessment. Overall, we report that the proposed approach reliably simulates user-type specific behaviour on a real-world academic search engine log dataset.

Retrieving suitable information to a user's information request is a crucial task of search engines. However, most of conventional search engines that are based on pattern matching schemes tend to provide too many improper results, most of which are not corresponding to a user's request. It is due to the uncertainty o f queries. To overcome this problem, we distinguished two different patterns after extensive investigation about the process of q uery formation. One is the general information seeking pattern and the other is the scholastic information retrieving pattern. The general infor mation seeking pattern has a tendency to correspond to cross- relational query formation process pattern, and the scholastic informatio n retrieving pattern corresponds to goal-oriented structured query pattern. This paper proposes a method to improve search results by utilizing these two query formation patterns. The study on the formation of general cross-relational queries, and goal-oriented str uctured query pattern by which comparing or modeling will improve the efficiency of navigation. These proposals will benefit not only sear ch engine developers and library managers, but also search engine users and researchers. This paper is expected to provide more suitable searching results than those obtained by using current typical information retrieval engines.