We present a new cross-lingual relevance feedback model that improves a machine-learned ranker for a language with few training resources, using feedback from a better ranker for a language that has more training resources. The model focuses on linguistically non-local queries, such as [world cup] and [copa mundial], that have similar user intent in different languages, thus allowing the low-re...

It has been known that using diierent representations of a query retrieves diierent sets of documents. Recent work suggests that signiicant improvement in retrieval performance can be achieved by combining multiple representations of an information need. In this paper, we rst investigate a fully automatic way of generating multiple query representations for a given information problem. We produ...

The motion track is an important feature to show the spatio-temporal relationship of a video object [2, 5, 7, 8]. In this paper, we propose a novel motion track representation to represent the motion track in the X-Y plane and the trend of velocity changes. Moreover, a new similarity measure for comparing two motion tracks based on the representation is proposed. Furthermore, the motion track s...

Relevance Feedback consists in automatically formulating a new query according to the relevance judgments provided by the user after evaluating a set of retrieved documents. In this article, we introduce several relevance feedback methods for the Bayesian Network Retrieval Model. The theoretical frame on which our methods are based uses the concept of partial evidences, which summarize the new ...

The INEX 2010 Focused Relevance Feedback track offered a refined approach to the evaluation of Focused Relevance Feedback algorithms through simulated exhaustive user feedback. As in traditional approaches we simulated a user-in-the loop by re-using the assessments of ad-hoc retrieval obtained from real users who assess focused ad-hoc retrieval submissions. The evaluation was extended in severa...

Relevance feedback has been shown to be an effective tool to enhance content-based information retrieval (CBIR) systems. We propose a new approach to relevance feedback by warping the database’s feature space, or shifting the objects’ data points in a controlled manner responding to user feedback. We demonstrate that given consistent feedback, the performance of the retrieval system can be sign...

This paper investigates methods for user and pseudo relevance feedback in video event retrieval. Existing feedback methods achieve strong performance but adjust the ranking based on few individual examples. We propose a relevance feedback algorithm (ARF) derived from the Rocchio method, which is a theoretically founded algorithm in textual retrieval. ARF updates the weights in the ranking funct...

In this paper, we report our experiments in the TREC 2009 Million Query Track. Our first line of study is on proximitybased feedback, in which we propose a positional relevance model (PRM) to exploit term proximity evidence so as to assign more weights to expansion words that are closer to query words in feedback documents. The second line of study is to improve the weighting of feedback docume...

Relevance feedback is an effective and widely accepted method in information retrieval to improve performance. Relevance feedback generally uses an adaptive learning method to estimate the user’s information need. In this research, we propose an alternative two-stage sampling method to obtain an unbiased estimate of the user’s information need. Our estimate shows not only improved retrieval per...

Content-based Image Retrieval (CBIR) using relevance feedback technique is applied to improve the results of traditional techniques in image retrieval. Since the results returned by system cannot fully satisfy users and the iteration process of feedback can be very time-consuming and tedious, log-based relevance feedback is introduce to the system. In previous work, we have already introduced m...