Modelling users' contextual querying behaviour for web image searching
Tseng, Liang-Chun (2012) Modelling users' contextual querying behaviour for web image searching. PhD thesis, Queensland University of Technology.
The rapid growth of visual information on Web has led to immense interest in multimedia information retrieval (MIR). While advancement in MIR systems has achieved some success in specific domains, particularly the content-based approaches, general Web users still struggle to find the images they want.
Despite the success in content-based object recognition or concept extraction, the major problem in current Web image searching remains in the querying process.
Since most online users only express their needs in semantic terms or objects, systems that utilize visual features (e.g., color or texture) to search images create a semantic gap which hinders general users from fully expressing their needs. In addition, query-by-example (QBE) retrieval imposes extra obstacles for exploratory search because users may not always have the representative image at hand or in mind when starting a search (i.e. the page zero problem). As a result, the majority of current online image search engines (e.g., Google, Yahoo, and Flickr) still primarily use textual queries to search.
The problem with query-based retrieval systems is that they only capture users’ information need in terms of formal queries;; the implicit and abstract parts of users’ information needs are inevitably overlooked. Hence, users often struggle to formulate queries that best represent their needs, and some compromises have to be made. Studies of Web search logs suggest that multimedia searches are more difficult than textual Web searches, and Web image searching is the most difficult compared to video or audio searches. Hence, online users need to put in more effort when searching multimedia contents, especially for image searches.
We argue that only by understanding Web image users’ contexts can the current Web search engines further improve their usefulness and provide more efficient searches.
In order to understand users’ search contexts, a user study was conducted based on university students’ Web image searching in News, Travel, and commercial Product domains. The three search domains were deliberately chosen to reflect image users’ interests in people, time, event, location, and objects.
We investigated participants’ Web image searching behavior, with the focus on query reformulation and search strategies. Participants’ search contexts such as their search background, motivation for search, and search outcomes were gathered by questionnaires. The searching activity was recorded with participants’ think aloud data for analyzing significant search patterns. The relationships between participants’ search contexts and corresponding search strategies were discovered by Grounded Theory approach. Our key findings include the following aspects:
- Effects of users' interactive intents on query reformulation patterns and search strategies
- Effects of task domain on task specificity and task difficulty, as well as on some specific searching behaviors
- Effects of searching experience on result expansion strategies
A contextual image searching model was constructed based on these findings. The model helped us understand Web image searching from user perspective, and introduced a context-aware searching paradigm for current retrieval systems. A query recommendation tool was also developed to demonstrate how users’ query reformulation contexts can potentially contribute to more efficient searching.
Impact and interest:
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|Item Type:||QUT Thesis (PhD)|
|Supervisor:||Tjondronegoro, Dian, Edwards, Sylvia L., Spink, Amanda H., & Lakshminarayanan, Bhuvaneshwari|
|Keywords:||Web image searching, Web image searching modeling, Web image searching behavior, Web image query reformulation, contextual image search modeling, contextual Web image search, Web image search context, image search, image query formulation, query reformulation, search pattern, search strategy|
|Divisions:||Current > QUT Faculties and Divisions > Science & Engineering Faculty|
|Institution:||Queensland University of Technology|
|Deposited On:||01 Jul 2013 22:28|
|Last Modified:||10 Sep 2015 02:13|
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