Keyword Spotting is a well-known method in document image retrieval. In this method, Search in document images is based on query word image. In this Paper, an approach for document image retrieval based on keyword spotting has been proposed. In proposed method, a framework using relevance feedback is presented. Relevance feedback, an interactive and efficient method is used in this paper to imp...

web search engines are one of the most popular tools on the internet which are widely-used by expert and novice users. constructing an adequate query which represents the best specification of users’ information need to the search engine is an important concern of web users. query expansion is a way to reduce this concern and increase user satisfaction. in this paper, a new method of query expa...

Web search engines are one of the most popular tools on the Internet which are widely-used by expert and novice users. Constructing an adequate query which represents the best specification of users’ information need to the search engine is an important concern of web users. Query expansion is a way to reduce this concern and increase user satisfaction. In this paper, a new method of query expa...

ƒ It's now time to go back to the user ƒ We have detailed a lot of tools and techniques that allow for sophisticated matching criteria to be applied, however in doing so we have implicitly assumed that the user " knows " how to formulate her queries/preferences ƒ In some cases the user does not know at all what to look for. In this case, a " browsing " activity should be supported. We do not co...

Relevance feedback is the retrieval task where the system is given not only an information need, but also some relevance judgement information, usually from users’ feedback for an initial result list by the system. With different amount of feedback information available, the optimal feedback strategy might be very different. In TREC Relevance Feedback task, the system is given different sets of...

We present a new supervised method for estimating term-based retrieval models and apply it to weight expansion terms from relevance feedback. While previous work on supervised feedback [Cao et al., 2008] demonstrated significantly improved retrieval accuracy over standard unsupervised approaches [Lavrenko and Croft, 2001, Zhai and Lafferty, 2001], feedback terms were assumed to be independent i...

This paper presents an experimental ”morphological analysis” retrieval system for mammograms, using Relevance-Feedback techniques. The features adopted are first-order statistics of the Normalized Radial Distance, extracted from the annotated mass boundary. The system is evaluated on an extensive dataset of 2274 masses of the DDSM database, which involves 7 distinct classes. The experiments ver...

Pseudo relevance feedback is empirically known as a useful method for enhancing retrieval performance. For example, we can apply the Rocchio method, which is well-known relevance feedback method, to the results of an initial search by assuming that the top-ranked documents are relevant a priori. In this paper, for searching NTCIR-3 patent test collection through pseudo feedback, we try to emplo...

All the tools and techniques we have detailed so far assume that the user “knows” how to formulate her queries/preferences Although with traditional DB’s and a few attributes this might be a reasonable assumption, when we consider many attributes/features it is not clear how a user might guess the right combination of weights How can one define the 64 weights for a color-based search using the ...