Image analysis – Image transformation or preprocessing – Image storage or retrieval
Reexamination Certificate
2001-03-02
2004-11-30
Miriam, Daniel (Department: 2621)
Image analysis
Image transformation or preprocessing
Image storage or retrieval
C382S190000, C382S218000, C358S403000, C707S793000
Reexamination Certificate
active
06826316
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to systems and methods for processing images. More specifically, the present invention relates to systems and methods for effecting automatic image retrieval.
BACKGROUND OF THE INVENTION
1. Description of the Related Art
Image-based document retrieval is required for a variety of consumer, commercial and government applications. Originally, images were retrieved manually. However, as image databases became larger, automated image retrieval systems were developed to accelerate the search and retrieval process.
One conventional automated approach involves the association of certain keywords with each image in a database. Images are then retrieved by a keyword search. However, this system suffers from the time intensive process of keyword input for large databases. In addition, the approach is highly dependent on the somewhat subjective manual assignment of keywords for each image and for the search itself. Finally, there is a limit with respect to the extent to which an image can be described adequately to allow for effective searching.
Another approach is that of automatic CBIR (content-based image retrieval). This system involves an analysis of each stored image with respect to its content (in terms of color, texture, shape, etc.). For example, the color content is stored in a histogram. In the search and retrieval process, the histogram from a query image is compared to the stored histogram data to find a best match. However, this system does not take into account spatial distribution of the color data.
The most often used approach to searching a database to select/retrieve images that are similar to a query is to compare the query image with the images in the database using their feature-based representation by means of distance functions. (See U.S. Pat. No. 5,579,471, entitled “Image Query System and Method,” issued Nov. 26, 1996 to R. J. Barber et al.; U.S. Pat. No. 5,852,823, entitled “Automatic Image Classification and Retrieval System From Database Using Query-By-Example Paradigm,” issued Dec. 22, 1998 to J. S. De Bonet; “Color Indexing”, published in
Intl. Journal, of Computer Vision,
by M. J. Swain and D. H. Ballard, Vol. 7, No. 1, 1991, pp. 11-32; and “Comparing Images Using Color Coherence Vectors,” published by G. Pass, et al., in
Proceedings ACM Multimedia Conf.,
(1996).
These techniques represent an image in terms of its depictive features, such as color or texture. Given a query image Q, its feature-based representation is compared against the representation of every image I in the database to compute the similarity of Q and I. The images in the database are then ranked in decreasing order of their similarity with respect to the query image to form the response to the query. A key shortcoming of these techniques is that no distinction is made between perceptually significant and insignificant image features in the image representation and matching schemes.
In general, a human observer determines the content-based similarity of two images primarily on the basis of the perceptually significant contents of the image and not the finer details. By mimicking this behavior, a similarity retrieval system might produce results that are in more agreement with human interpretation of similarity. However, this fact has not been exploited by any of the above mentioned techniques.
In a copending U.S. Patent Application entitled “Perceptually Significant Feature-based Image Archival and Retrieval,” U.S. Ser. No. filed 14 Apr. 1999 by Wei Zhu and Rajiv Mehrotra, the teachings of which are incorporated herein by reference, Zhu et al. attempt to overcome the above-mentioned shortcoming by representing an image in terms of its perceptually significant features. Thus, similarity of two images becomes a function of the similarity of their perceptually significant features.
However, in this approach, image features are extracted from the properties of the entire image. There is no flexibility in computing image features or comparing image similarities based on main subject or background regions. As a result, more targeted searches, such as finding images with similar main subjects but dissimilar backgrounds as the query, cannot be performed.
Recently, U.S. Pat. No. 6,038,365, entitled “Image Retrieval-Oriented Processing Apparatus Which Generates and Displays Search Image Data That Is Used As Index,” was issued to T. Yamagami on Mar. 14, 2000. An image processing apparatus according to this invention includes a designating unit for designating an image area to be used as a retrieval image from a recorded image recorded in a recording medium, a storing unit for storing image area data representing the image area designated by the designating unit in connection with the corresponding recorded image, and a displaying unit for displaying, as the retrieval image, an image of the image area on the basis of the corresponding image area data stored in the storing unit.
Further, an image processing apparatus according to Yamagami's invention includes a designating unit for designating an image area from an original image constituting a screen as a retrieval image, a storing unit for storing the retrieval image designated by the designating unit in connection with the corresponding original image, a displaying unit for displaying the retrieval image designated by the designating unit, an instructing unit for instructing the retrieval image displayed by the displaying unit, and a display control unit for displaying, on the displaying unit, the original image corresponding to the retrieval image instructed by the instructing unit.
Hence, Yamagami appears to disclose use of a selected area of an image for image retrieval. However, the selection is done manually using a designating unit. Further, the use of the selected area is motivated by an image reduction problem that makes characters too small to read. Since image data can generally be recognized only when a human being looks at them, when image data are reproduced, a list of a plurality of reduced images may generally be displayed so that the user can check the contents of image files, using the reduced images themselves as the retrieval images. However, in retrieval display of reduced images, since an entire image is simply reduced to, for example, one eighth in both its longitudinal and lateral dimensions, the reduced image may be too small to be recognized easily, making the use of that reduced image as a retrieval image impossible.
Consequently, Yamagami does not teach an automatic, general-purpose image retrieval apparatus. Nor is Yamagami's invention built upon an automatic scene-content analysis scheme. Accordingly, a need remains in the art for a more accurate system or method for automatically retrieving images from a database.
SUMMARY OF THE INVENTION
The need in the art is addressed by the system and method for determining image similarity of the present invention. The inventive method includes the steps of automatically providing perceptually significant features of main subject or background of a first image; automatically providing perceptually significant features of main subject or background of a second image; automatically comparing the perceptually significant features of the main subject or the background of the first image to the main subject or the background of the second image; and providing an output in response thereto.
In the illustrative implementation, the features are provided by a number of belief levels, where the number of belief levels are preferably greater than two. In the illustrative embodiment, the step of automatically providing perceptually significant features of the main subject or background of the first image includes the steps of automatically identifying main subject or background of the first image and the step of identifying perceptually significant features of the main subject or the background of the first image. Further, the step of automatically providing perceptually significant features of the main subject or background
Gray Robert T.
Luo Jiebo
Mehrotra Rajiv
Sotak George E.
Zhu Wei
Eastman Kodak Company
Miriam Daniel
Woods David M.
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