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The main objective of our work is to makethe limited user log play the fullest role.

This probabilistic scheme is to make

images with higher similarity to mostpositive examples more likely similar tothe query image.

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Content Based Image Retrieval

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INTRODUCTION DUE to the rapidly growing amount of digital

image data on the Internet and in digitallibraries, there is a great need for large image

database management and effective imageretrieval tools.

Content-based image retrieval (CBIR) is the setof techniques for searching for similar images

from an image database using automaticallyextracted image features.

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Most current content-based image retrievalsystems are still incapable of providing userswith their desired results.

The major difficulty lies in the gap between low-

level image features and high-level imagesemantics. To address the problem, this studyreports a framework for effective image retrievalby employing a novel idea of memory learning.

It forms a knowledge memory model to storethe semantic information by simply accumulatinguser-provided interactions.

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DRAWBACKS

Incapability of capturing semantics.Most RF techniques in CBIR absolutelycopy ideas from textural informationretrieval.

Scarcity and imbalance of feedbackexamples.

Very few users are willing to go through

endless iterations of feedback with thehopes of getting the best results.

Lack of the memory mechanism.

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A feedback knowledge memory model is

presented to gather the users' feedbackinformation during the process of image searchand feedback. It is efficient and can be simplyimplemented.

A learning strategy based on the memorizedinformation is a proposed. It can estimate thehidden semantic relationships among images.

Consequently, this technique could address theproblem of user log sparsity in a certain ex-tent.

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During the interactive process, a seamless

combination of normal RF (low-level featurebased) and the memory learning (semanticsbased) is proposed to improve the retrievalperformance.

A semantics-based image annotationpropagation scheme is proposed using bothmemorized and learned semantics.

In contrast with existing algorithms ofpropagating annotation by visual similarity, itsprecision is much better.

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we briefly review the related work. wepresent the feedback knowledge memorymodel.

The learning strategy to estimate thehidden semantics.

The image retrieval framework by memorylearning.

The experimental results are shown in.

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1. Feedback Knowledge Memory Model

2. Semantic Correlation Analysis by a LearningStrategy

Image Semantic Clustering

Image Authoritative Rank

Hidden Semantic Correlation Between Two Images

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3. Image Retrieval Framework by theMemory Learning

Image Similarity Measure

Relevance Feedback Integrating SVM LearningWith Memory Learning

Image Annotation and Annotation Propagation

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Review of Related Work

Feedback Knowledge Memory ModelSemantic Correlation Analysis by a Learning Strategy Image Retrieval Framework by the Memory LearningExperimental Results

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The idea of long-term learning in CBIR isborrowed from the work of collaborative filteringand link structure analysis in the webinformation retrieval.

Unlike the collaborative filtering, many websearch engines search for web pages by the linkstructure analysis.

Pages that are co-cited by a certain page arelikely to relate to the same topic, and pages thatare often visited in succession by a certain userare possibly similar.

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In order to supply effective image retrieval tousers, this paper has presented a new memorylearning framework in which low-level feature-based RF and semantics-based memory learningare combined to help each other to achievebetter retrieval performance.

There are two novel characteristics thatdistinguish the memory learning framework fromthe existing RF techniques.

The proposed framework is easy to implementand can be efficiently incorporated into an imageretrieval system.

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The proposed framework is easy to implementand can be efficiently incorporated into animage retrieval system.

Experimental evaluations on a large-scale imagedatabase have already shown very promisingresults.

Our future work will investigate the possibility todevelop more sophisticated and theoreticallearning schemes.

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M. Flickner, H. Sawhney, and W. Niblack, "Query

by image and video content: The QBIC system,"IEEE Computer,

A. P. Penland, R. W. Picard, and S. Sclaroff,"Photobook: Content-based manipulation of

image databases, H. Zhang and Z. Su, "Relevance feedback in

CBIR," presented at the Int. Workshop on VisualDatabases.

X. S. Zhou and T. S. Huang, "Relevancefeedback in image retrieval: A comprehensivereview,"

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