20-10-2012, 02:02 PM
Efficient Relevance Feedback for Content-Based Image Retrieval
by Mining User Navigation Patterns
Efficient Relevance Feedback.pdf (Size: 3.99 MB / Downloads: 29)
Abstract
Nowadays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable,
relevance feedback techniques were incorporated into CBIR such that more precise results can be obtained by taking user’s
feedbacks into account. However, existing relevance feedback-based CBIR methods usually request a number of iterative feedbacks
to produce refined search results, especially in a large-scale image database. This is impractical and inefficient in real applications. In
this paper, we propose a novel method, Navigation-Pattern-based Relevance Feedback (NPRF), to achieve the high efficiency and
effectiveness of CBIR in coping with the large-scale image data. In terms of efficiency, the iterations of feedback are reduced
substantially by using the navigation patterns discovered from the user query log. In terms of effectiveness, our proposed search
algorithm NPRFSearch makes use of the discovered navigation patterns and three kinds of query refinement strategies, Query Point
Movement (QPM), Query Reweighting (QR), and Query Expansion (QEX), to converge the search space toward the user’s intention
effectively. By using NPRF method, high quality of image retrieval on RF can be achieved in a small number of feedbacks. The
experimental results reveal that NPRF outperforms other existing methods significantly in terms of precision, coverage, and number of
feedbacks.
INTRODUCTION
MULTIMEDIA contents are growing explosively and the
need for multimedia retrieval is occurring more and
more frequently in our daily life. Due to the complexity of
multimedia contents, image understanding is a difficult but
interesting issue in this field. Extracting valuable knowledge
from a large-scale multimedia repository, so-called
multimedia mining, has been recently studied by some
researchers. Typically, in the development of an image
requisition system, semantic image retrieval relies heavily
on the related captions, e.g., file-names, categories, annotated
keywords, and other manual descriptions [19], [20].
Unfortunately, this kind of textual-based image retrieval
always suffers from two problems: high-priced manual
annotation and inappropriate automated annotation. On one
hand, high-priced manual annotation cost is prohibitive in
coping with a large-scale data set.
RELATED WORK
Relevance feedback [5], [17], [25], in principle, refers to a set
of approaches learning from an assortment of users’
browsing behaviors on image retrieval [10]. Some earlier
studies for RF make use of existing machine learning
techniques to achieve semantic image retrieval, including
Statistics, EM, KNN, etc. Although these forerunners were
devoted to formulating the special semantic features for
image retrieval, e.g., Photobook [11], QBIC [1], VisualSEEK
[16], there still have not been perfect descriptions for
semantic features. This is because of the diversity of visual
features, which widely exists in real applications of image
retrieval. Therefore, active query refinement, based on the
analysis of usage logs, attracts researchers’ attention in this
area of RF.
Query Reweighting
Some previous work keeps an eye on investigating what
visual features are important for those images (positive
examples) picked up by the users at each feedback (also
called iteration in this paper). The notion behind QR is
that, if the ith feature fi exists in positive examples
frequently, the system assigns the higher degree to fi. QRlike
approaches were first proposed by Rui et al. [14],
which convert image feature vectors to weighted-term
vectors in early version of Multimedia Analysis and Retrieval
System (MARS).