Image analysis – Image transformation or preprocessing – Image storage or retrieval
Reexamination Certificate
2000-12-14
2004-06-01
Mariam, Daniel (Department: 2621)
Image analysis
Image transformation or preprocessing
Image storage or retrieval
C382S190000, C382S209000, C358S403000, C707S793000
Reexamination Certificate
active
06744935
ABSTRACT:
FIELD OF THE INVENTION
The invention relates to a content-based image retrieval apparatus and method via relevance feedback by using fuzzy integral and a computer readable record medium storing programs for realizing the apparatus and method; and, in more particular, to a content-based image retrieval apparatus and method in which image features are associated by using fuzzy integral and parameters necessary for the fuzzy integral are adjusted in similarity calculations via relevance feedback so as to improve search results and a record medium recorded with programs which can be read by a computer for realizing the method.
In other words, the invention relates to a content-based image retrieval apparatus and method via relevance feedback by using fuzzy integral for providing an improved search result over first search result via relevance feedback about the first search result (namely, user opinion if each search result is a desired one) which is outputted according to a user query in a content-based image retrieval system by using various image features including color, texture, shape, etc., and a computer readable record medium storing programs for realizing the method.
DESCRIPTION OF THE RELATED ART
A content-based image retrieval apparatus extracts features from a query image and database images (namely, images in a database), calculates similarities based on a similarity measuring method between the defined image features, and then displays the database images sequentially in the order of higher similarity to the query image.
In general, retrieval methods by associating image features provide better search results over retrieval methods by using only one image feature. Therefore, a weight averaging has been primarily used in which weights are added according to the importance of each feature in order to search image features by association.
However, in the weight averaging of the prior art, common users could not correctly adjust weights and modeling various query forms was difficult. For example, a query such as “Search images similar to the given image by using 3 random features out of 5 features,” cannot be processed via the weight averaging, since it is unknown how to select and apply 3 features to similarity measuring.
Furthermore, the weight averaging does not consider interactions among the image features. Namely, similarities are associated linearly on the assumption that each image feature is independent of one another. However, each image is not independent of one another so that interactions among the features should be considered. For example, when two features have a large value of relatedness between them, the weight about these features should be smaller than sum of all weights. Also, some features can have varied weights if other features are present.
Therefore, in the present content-based image retrieval techniques, a novel method is required in which image similarities are associated by using the fuzzy integral and parameters necessary for fuzzy integral are adjusted via relevance feedback to calculate similarities so that various relevance feedbacks can be processed which cannot have been expressed in the conventional weight adjustment.
SUMMARY OF THE INVENTION
Therefore, it is an object of the invention to provide a content-based image retrieval apparatus and method for associating image features by using fuzzy measurement and fuzzy integral and automatically calculating the importance about the image features via relevance feedback to improve a search result, and a record medium recorded with programs which can be read by a computer for realizing the method.
In accordance with an aspect of the present invention, there is provided a content-based image retrieval apparatus via relevance feedback by using fuzzy integral, comprising: image input unit for receiving images from a user; feature extraction unit for extracting image features necessary for image similarity operation from the images inputted via the image input means; first storage unit for storing the image features extracted by the feature extraction unit; thumb nail generator for dividing the images inputted via the image input means to generate thumb nails; second storage unit for storing the thumb nails generated by the thumb nail generator; similarity calculator for calculating the similarities between a selective query image and database images according to feature by using the image features of the first storage unit; similarity measuring unit for associating the similarities calculated via the similarity calculator by using the fuzzy integral to measure final image similarities; query processing unit for interpreting the query from the user to measure similarities, and for sequentially calling and transmitting the images via the second storage unit according to the calculated similarities; and user interface for performing relevance feedback about the image data transmitted from the query processing unit, and for transmitting selective information according to relevance feedback to the query processing unit.
In accordance with another aspect of the present invention, there is provided a content-based image retrieval method via relevance feedback by using fuzzy integral, comprising the steps of: measuring similarities according to feature between images stored in a database and a new query image; associating the similarities measured according to feature by using the fuzzy integral to measure similarities according to the fuzzy integral; bringing images sequentially in the order of higher similarity according to the finally obtained similarities and transmitting a search result; and recalculating similarities according to feature via relevance feedback about the query result to output converted images.
In accordance with further another aspect of the present invention, there is provided a computer readable record medium storing programs for executing a content-based image retrieval method, the method comprising the steps of: measuring similarities according to feature between images stored in a database and a new query image; associating the similarities measured according to feature by using the fuzzy integral to measure similarities according to fuzzy integral; bringing images sequentially in the order of higher similarity according to the finally obtained similarities and transmitting a search result; and recalculating similarities according to feature via relevance feedback about the query result to output converted images.
According to the invention, various features such as color, texture and shape are generally extracted from the images and a similarity measuring method is defined for measuring similarities among these features for a content-based image retrieval. When a user query is inputted, a result is displayed to the user in the order of higher similarity according to the defined similarity measuring method. Here, measuring similarities by associating various features can generally obtain a better result than those obtained by using one feature, and adjusting weights about each feature can enable relevance feedback. However, simple weight adjustment itself has a limitation for processing various queries intended by the user. Therefore, in the invention, the fuzzy integral is used in associating these features, and parameters necessary for the fuzzy integral are adjusted to calculate similarity via relevance feedback so that various relevance feedbacks can be realized which cannot have been expressed in the conventional weight adjustment.
In other words, the invention adopts the fuzzy integral which is one of those methods for associating various image features in a content-based image retrieval apparatus. The fuzzy integral is defined about fuzzy measurement, which indicates importance about all power set of a variable set {x
1
, . . . , x
n
} which is subjected to measurement. Here, n is the total number of variables to be measured, and the number of the variables necessary for indicating fuzzy measurement is 2
n
. Sum of the fuzzy measurement g({x
1
}
Choi Young-Sik
Kim Jin-Han
Lee Sang-Hong
Yoon Eun-Il
Korea & Telecom
Mariam Daniel
Staas & Halsey , LLP
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