Image processing method and control method therefor

Abstract: A retrieval condition for retrieving desired image data is input from a user interface, and the input retrieval condition is analyzed by a text processing unit. On the basis of the analysis result, a retrieval result notification unit outputs a question to prompt the user to input a retrieval condition different from the previously input retrieval condition. A retrieval condition is input from the user interface in accordance with the output question. A retrieval unit retrieves image data on the basis of the input retrieval condition.

Description: BACKGROUND OF THE INVENTION FIELD OF THE INVENTION The present invention relates to an image processing apparatus for managing image data and a control method therefor. DESCRIPTION OF THE RELATED ART Conventionally, when image data is to be retrieved using an image processing apparatus which manages image data obtained by reading a natural image with a scanner or the like, direct image data retrieval cannot be performed because an image recognition technique for image data is difficult to realize. For this reason, generally, a description and keyword representing the image data are added to the image data in reading and used to retrieve the image data. In the conventional image processing apparatus, however, especially when the number of image data to be managed is large, description/keyword addition is labor-intensive. To reduce the labor, image data may be managed without adding any description and keyword. In this case, thumbnail images of all image data being managed must be displayed, and the user must select a desired one of the thumbnail images to retrieve the image data. Such an apparatus is inconvenient to use and poor in efficiency. Additionally, since many new words are being coined recently, demand for retrieval under a retrieval condition input using a newly coined word, i.e., retrieval according to the taste or need of a user has arisen. WO-A-95/06912 discloses a retrieval system to have a thesaurus of synonyms used to index entities including graphic data which allows a user to specify a number of descriptors comprising a meaning reference set for each entity. The thesaurus then provides entity identifiers linking the meaning reference sets to the entity data. A paper entitled "Efficient and Effective Querying by Image Content", Faloutsos C et al, Journal of Intelligent Information Systems: Artificial Intelligence and Database Technologies, vol. 3, no. 3/04, 1 July 1994, pages 231-262, XP000564740, discloses the processing of images to store outline and keywords for searching. During retrieval, a user enters a query which specifies colour or shape or by referring to an example found in a previous search. Aspects of the present invention are set out in the appended claims. A preferred embodiment provides an image processing apparatus and method which allow easy and efficient retrieval according to the taste of a user without adding any description and keyword for managing image data. Other features and advantages of the present invention will be apparent from the following description taken in conjunction with the accompanying drawings, in which like reference characters designate the same or similar parts throughout the figures thereof. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention. Fig. 1 is a block diagram showing the arrangement of an image processing apparatus according to the first embodiment of the present invention; Fig. 2 is a view showing details of the arrangement of a synonym/concept dictionary in the first embodiment of the present invention; Fig. 3 is a flow chart showing the flow of image registration processing executed in the first embodiment of the present invention; Fig. 4 is a flow chart schematically showing retrieval processing executed in the first embodiment of the present invention; Fig. 5 is a flow chart showing details of retrieval processing executed in the first embodiment of the present invention; Fig. 6 is a view showing an example of the user interface in the first embodiment of the present invention; Fig. 7 is a view showing another example of the user interface in the first embodiment of the present invention; Fig. 8 is a view showing an example of the updated synonym/concept dictionary in the first embodiment of the present invention; Fig. 9 is a flow chart showing details of processing in step S305 in the first embodiment of the present invention; Fig. 10 is a view showing still another example of the user interface in the first embodiment of the present invention; Fig. 11 is a view showing still another example of the user interface in the first embodiment of the present invention; Fig. 12 is a view showing still another example of the user interface in the first embodiment of the present invention; Fig. 13 is a view showing still another example of the user interface in the first embodiment of the present invention; Fig. 14 is a block diagram showing the arrangement of an image processing apparatus according to the second embodiment of the present invention; Fig. 15 is a flow chart showing the flow of image registration processing executed in the second embodiment of the present invention; Fig. 16 is a flow chart schematically showing retrieval processing executed in the second embodiment of the present invention; Fig. 17 is a flow chart showing details of retrieval processing executed in the second embodiment of the present invention; Fig. 18 is a flow chart showing details of retrieval processing executed in the third embodiment of the present invention; Fig. 19 is a view showing the memory map structure of a storage medium storing a program code for realizing the first embodiment of the present invention; Fig. 20 is a view showing the memory map structure of a storage medium storing a program code for realizing the second embodiment of the present invention; and Fig. 21 is a view showing the memory map structure of a storage medium storing a program code for realizing the third embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of the present invention will be described below in detail with reference to the accompanying drawings. <First Embodiment> Fig. 1 is a block diagram showing the arrangement of an image processing apparatus according to the first embodiment of the present invention. Referring to Fig. 1, a user interface 1 is constituted by a keyboard or mouse. An image storage unit 4 stores image data temporarily stored in an image memory 5. An image input unit 2 inputs image data to be registered in the image storage unit 4 in accordance with an instruction from the user interface 1. Image data to be handled in the first embodiment is natural image data. However, the image data is not limited to this, and artificial image data such as CG or CAD data may be processed. The image memory 5 temporarily stores image data input from the image input unit 2. An image feature extraction unit 7 extracts an image feature amount for retrieving image data similar to image data temporarily stored in the image memory 5. An image feature index 9 registers the image feature amount of image data extracted by the image feature extraction unit 7 in correspondence with the image data. A text processing unit 3 outputs a retrieval word input from the user interface 1 to a retrieval unit 10. A full text retrieval/registration unit/keyword registration unit 6 registers a word input from the text processing unit 3 in a synonym/concept dictionary 8. The synonym/concept dictionary 8 manages synonyms in units of concepts corresponding to the synonyms. The word to be managed by the synonym/concept dictionary 8 has an image feature weight representing the effectivity of the word itself for the retrieval word. Details of the synonym/concept dictionary 8 will be described later. The retrieval unit 10 comprises an image retrieval unit 10a, a text/image feature/concept conversion unit 10b, and a text retrieval unit 10c. The text retrieval unit 10c retrieves a word corresponding to the retrieval word input from the text processing unit 3. The text/image feature/concept conversion unit 10b acquires the image feature weight for the retrieval word with reference to the synonym/concept dictionary 8 and calculates an image feature amount for retrieving image data. The image retrieval unit 10a retrieves image data by looking up the image feature index 9 on the basis of the image feature amount calculated by the text/image feature/concept conversion unit 10b. A retrieval result notification unit 12 displays image data obtained from the retrieval unit 10 as a retrieval result. The retrieval result notification unit 12 also displays, for the user, a question window for obtaining information usable for retrieval in association with the input retrieval word. Details of the arrangement of the synonym/concept dictionary 8 of the first embodiment will be described next with reference to Fig. 2. Fig. 2 is a view showing details of the arrangement of the synonym/concept dictionary in the first embodiment of the present invention. The synonym/concept dictionary 8 registers synonyms on the basis of the abstraction levels of concepts corresponding to the synonyms. For synonyms of different conceptual abstraction levels, a conceptual distance representing the difference in abstraction level is defined. In Fig. 2, the conceptual distance between "vehicle" and "wheeled vehicle" is 10. In addition, for synonyms, an n-dimensional vector is defined as an image feature weight for weighting each of n image feature amounts of an input retrieval word to reflect its effectivity. The n-dimensional vector is normalized with reference to 100. For example, an automobile is an artificial object and can have various colors. The weight for an image feature amount associated with colors is 0, so the image feature amount must not be referred to. In this case, retrieval is executed by placing importance on image feature amounts except colors as effective image feature amounts for the retrieval word. However, when the retrieval word is "red wheeled vehicle", information "red" is taken into consideration, so retrieval is executed in consideration of red color as an image feature amount. Image registration processing executed to register image data in the image processing apparatus of the first embodiment will be described next with reference to Fig. 3. Fig. 3 is a flow chart showing the flow of image registration processing executed in the first embodiment of the present invention. In step S101, image data to be registered is input from the image input unit 2, and the input image data is temporarily stored in the image memory 5. In step S102, the image feature amounts of the image data are extracted by the image feature extraction unit 7. In step S103, the image feature amounts extracted from the image data are registered in the image feature index 9 in correspondence with the image data. In step S104, the image data is stored in the image storage unit 4. Retrieval processing executed to retrieve image data in the image processing apparatus of the first embodiment will be described next with reference to Fig. 4. Fig. 4 is a flow chart schematically showing retrieval processing executed in the first embodiment of the present invention. In step S201, all image data stored in the image storage unit 4 are set as image data for retrieval. In step S202, a retrieval word using a natural language is input from the user interface 1. In step S203, image data is retrieved on the basis of the input retrieval word. When retrieval is executed, the retrieval result is displayed on the retrieval result notification unit 12 in step S204. In step S206, an instruction from the user for the retrieval result is received. Details of retrieval in step S203 will be described later with reference to Fig. 5. If the user instructs to set a new retrieval word to narrow down the retrieval result, a retrieval instruction for further narrowing down the current retrieval result is set from the user interface 1 in step S208. The flow returns to step S202 to input a new retrieval word. In this case, the current retrieval result is held. The retrieval result based on the newly input retrieval word and the held retrieval result are ANDed to narrow down the retrieval result. If the user instructs to end retrieval processing, processing is ended. If the user instructs to perform retrieval again, the retrieval result is cleared in step S209, and the flow returns to step S201. If the user selects one of image data (thumbnail image data) displayed as the retrieval result to instruct display of detail of the desired image data, the detail (full-scale image data) of the selected image data (thumbnail image data) is displayed in step S207. Details of retrieval in step S203 will be described next with reference to Fig. 5. Fig. 5 is a flow chart showing details of retrieval processing executed in the first embodiment of the present invention. In step S301, the morpheme and modification of the input retrieval word using a natural language are analyzed with reference to the synonym/concept dictionary 8 to extract an "retrieval object name" represented by the retrieval word and an outer appearance feature representing the feature of the "retrieval object name". Next, it is determined whether the extracted "retrieval object name" is present in the synonym/concept dictionary 8. If the "retrieval object name" is present in the synonym/concept dictionary 8 (YES in step S302), the flow advances to step S303. If the "retrieval object name" is not present in the synonym/concept dictionary 8 (NO in step S302), the flow advances to step S309. In step S309, the user is asked about an "object name" having a concept closest to the "retrieval object name". The acquired "object name" is set as a "retrieval object name". By processing in step S309, a word having a concept close to the input "retrieval object name" can be extracted. In step S310, the "retrieval object name" set in step S309 is registered in the synonym/concept dictionary 8 as a new "retrieval object name". Details of processing in step S309 will be described with reference to Figs. 6 to 8. Fig. 6 is a view showing an example of the user interface of the first embodiment of the present invention. When processing in step S309 is executed, a word input window as shown in Fig. 6 is displayed on the retrieval result notification unit 12. When a word having a concept close to the "retrieval object name" is input using this word input window, a word having a concept close to the input word is displayed. In the example shown in Fig. 6, "motorcar" has been input as a "retrieval object name", and since this word is not registered in the synonym/concept dictionary 8, "automobile" having a close concept is input. Five buttons, i.e., "back", "forward", "search", "cancel", and "OK" are displayed on the right side of the word input window. By pointing one of the buttons with the cursor displayed on the retrieval result notification unit 12, processing to be described below is executed. With the "back" button, the currently displayed word input window returns to the previous word input window. The "forward" button is used to display a new word input window. With the "search" button, a word having a concept close to the input word is searched for, and the result is displayed as shown in Fig. 7. The "cancel" button is used to cancel the word input. The "OK" button is used to select a word having a concept close to the "retrieval object name" which is not registered in the synonym/concept dictionary 8. The "retrieval object name" which has not been registered in the synonym/concept dictionary 8 is registered in the synonym/concept dictionary 8 as a new synonym of conceptual level between the conceptual level of the selected word and the next conceptual level. For example, when "automobile" is selected as a word having a concept close to "motorcar" which has not been registered in the synonym/concept dictionary 8, "motorcar" is registered as a word of a conceptual level between the conceptual level of "automobile" and that of "car". "Motorcar" is registered at a position at which the conceptual distance from "automobile" equals that from "car". In addition, the image feature weight of "motorcar" is prepared on the basis of the image feature weights of "automobile" and "car" and registered. Refer back to the flow chart shown in Fig. 5. In step S303, the image feature weight of the "retrieval object name" is acquired from the synonym/concept dictionary 8. If the outer appearance feature of the "retrieval object name" is extracted in step S304, an image feature weight associated with the outer appearance feature is added to the image feature weight acquired in step S303. In step S305, the user is asked about an effective image feature weight for the image feature amount. Details of this processing will be described with reference to the flow chart shown in Fig. 9. Fig. 9 is a flow chart showing details of processing in step S305 of the first embodiment of the present invention. Processing in Fig. 9 is executed to further obtain effective information (image feature weights) for retrieval, or supplement image feature amounts in retrieval. In step S403, it is determined on the basis of the image feature weight of the acquired "retrieval object name" whether the retrieval object represented by the "retrieval object name" has a unique color. If the retrieval object has a unique color (YES in step S403), the flow advances to step S404. If the color of the retrieval object is not unique (NO in step S403), the flow advances to step S407. In step S407, the user is asked to "designate a color close to the retrieval object" using a question window as shown in Fig. 10, and a designation from the user is received. The color is designated using, e.g., a word or color sample, as shown in Fig. 10. In step S408, the designated color is stored as an image feature weight. In step S404, the user is asked whether "the size of the retrieval object is large" using a question window as shown in Fig. 11. In step S405, the user selects an answer about the size of the retrieval object represented by the "retrieval object name" using the question window. If the size of the retrieval object is large (YES in step S405), the flow advances to step S406. It is determined in step S406 that the color of the retrieval object represented by the "retrieval object name" is very important retrieval information. The image feature weight associated with the color of the retrieval object is increased, and the image feature amount of the "retrieval object name" is set. If the retrieval object is not large (NO in step S405), the flow advances to step S409. In step S409, the user is asked about the "background" using a question window as shown in Fig. 12, and the answer from the user is received. The background is designated using, e.g., an object name, as shown in Fig. 12. It is determined in step S410 whether the background has a unique color. If the background has a unique color (YES in step S410), the flow advances to step S411. If the color of the background is not unique (NO in step S410), the flow advances to step S412. In step S412, the user is asked to "designate a color close to the background" using a question window as shown in Fig. 13, and the answer from the user is received. In step S413, the designated color is stored as an image feature weight. It is determined in step S411 that the color of the background is very important retrieval information. The image feature weight associated with the color of the background is increased, and the image feature amount of the "retrieval object name" is set. In the above description, information associated with the retrieval object color or background color is designated by the user. However, information associated with the feature of the surface of the retrieval object or other feature may be designated by the user, as a matter of course. With the above arrangement, an image feature weight useful for retrieval using the input retrieval word can be prepared. Retrieval can be executed on the basis of an image feature amount considering the image feature weight, so precise retrieval can be performed. Refer back to the flow chart in Fig. 5. In step S306, image data is retrieved on the basis of the image feature amount while looking up the image feature index 9. It is determined in step S307 whether image data to be retrieved is present. If image data to be retrieved is present (YES in step S307), the flow advances to step S308. In step S308, the retrieved image data is displayed on the retrieval result notification unit 12. If image data to be retrieved is not present (NO in step S307), the flow advances to step S312. It is determined in step S312 whether the user wants to redesignate the image feature amount. If the user wants to redesignate the image feature amount (YES in step S312), the flow advances to step S311. In step S311, the user is asked about an effective image feature weight for the image feature amount. This processing has been described in detail using the flow chart in Fig. 6. If the user does not want to redesignate the image feature amount (NO in step S312), the flow advances to step S313. In step S313, the retrieval result notification unit 12 displays that no retrieved image data is present. As described above, according to the first embodiment, the outer appearance feature of the retrieval object name represented by the input retrieval word using a natural language can be input, so specific retrieval conditions desired by the user can be input. Even when the input retrieval word has not been registered in the synonym/concept dictionary 8, a word having a concept close to the retrieval word can be input for retrieval. Since the word having a concept close to the retrieval word can be newly registered in the synonym/concept dictionary 8, the synonym/concept dictionary 8 according to taste of the user can be generated. The user can retrieve desired image data only by inputting retrieval conditions without adding any keywords. The synonym/concept dictionary 8 can learn an unknown word such as a newly coined word through an interactive interface to the user and be updated. Therefore, the retrieval range can be widened in accordance with the need of the time to realize retrieval according to taste of the user. In the first embodiment, retrieval of natural image data has been described. However, the gist of the present invention can be applied to information retrieval from information media in various forms. In addition, retrieval based on the description or keyword added to image data may be performed simultaneously with the above-described processing, and the user may be notified of the integrated retrieval result, although this has not been described in the first embodiment. <Second Embodiment> Fig. 14 is a block diagram showing the arrangement of an image processing apparatus according to the second embodiment of the present invention. The same reference numerals as in the first embodiment denote the same constituent elements in the second embodiment, and a detailed description thereof will be omitted. Referring to Fig. 14, when image data input from an image input unit 2 has a description and keyword representing the contents of the image data, a text processing unit 3a analyzes the morpheme of the description or keyword by looking up a synonym/concept dictionary 8 and outputs a word obtained from the analysis result to a full text retrieval/registration unit/keyword registration unit 6a. When a retrieval word for retrieving image data stored in an image storage unit 4 is input from a user interface 1, the text processing unit 3a outputs the retrieval word to an image retrieval unit 10. The full text retrieval/registration unit/keyword registration unit 6a registers a word input from the text processing unit 3a in a full text retrieval index/keyword index 11 in correspondence with input image data. A retrieval unit 10a comprises an image retrieval unit 10ab, a text/image feature/concept conversion unit 10bb, and a text retrieval unit 10cb. The text retrieval unit 10cb performs full text retrieval/keyword retrieval on the basis of the retrieval word input from the text processing unit 3a while looking up the full text retrieval index/keyword index 11. The text/image feature/concept conversion unit 10bb acquires an image feature weight for the retrieval word by looking up the synonym/concept dictionary 8 and calculates an image feature amount for retrieving image data similar to the image data retrieved by the text retrieval unit 10cb. The image retrieval unit 10ab retrieves similar image data on the basis of the image feature amount calculated by the text/image feature/concept conversion unit 10bb while looking up an image feature index 9. The image retrieval unit 10ab also retrieves similar image data on the basis of the image feature amount of one of image data as the retrieval result displayed on a retrieval result notification unit 12, which is designated from a user interface 1, by looking up the image feature index 9. Image registration processing executed to register image data in the image processing apparatus of the second embodiment will be described next with reference to Fig. 15. Fig. 15 is a flow chart showing the flow of image registration processing executed in the second embodiment of the present invention. First, in step S111, image data for registration is input from the image input unit 2. The input image data is temporarily stored in an image memory 5. It is determined in step S112 whether the input image data has a description or keyword of the input image data. If the image data has neither description nor keyword (NO in step S112), the flow advances to step S114. If the image data has a description or keyword (YES in step S112), the flow advances to step S113. In step S113, the description or keyword added to the image data is registered in the full text retrieval index/keyword index 11 in correspondence with the image data. In step S114, the image feature amount of the image data is extracted by an image feature extraction unit 7. In step S115, the image feature amount extracted from the image data is registered in the image feature index 9 in correspondence with the image data. In step S116, the image data is stored in the image storage unit 4. When a plurality of typical and high-quality representative image data which are likely to be frequently retrieved (image data to be retrieved at a frequency larger than a predetermined value) are to be registered in initial installation, descriptions or keywords representing the contents of the representative image data are added. The added descriptions or keywords are registered in the full text retrieval index/keyword index 11 in correspondence with the representative image data. To retrieve similar image data, the image feature amounts of the representative image data are extracted and registered in the image feature index 9. Retrieval processing executed to retrieve image data in the image processing apparatus of the second embodiment will be described next with reference to Figs. 16 and 17. First, the retrieval processing will be briefly described using Fig. 16. Fig. 16 is a flow chart schematically showing retrieval processing executed in the second embodiment of the present invention. In step S211, all image data stored in the image storage unit 4 are set as image data to be retrieved. In step S212, a "retrieval word" as a retrieval condition is input from the user interface 1. In step S213, retrieval processing (full text retrieval/keyword retrieval) is performed on the basis of the input "retrieval word" which is used as a description or keyword. Details of this retrieval processing will be described below with reference to Fig. 17. Fig. 17 is a flow chart showing details of retrieval processing executed in the second embodiment of the present invention. In step S311, "0" is set in a counter (not shown), and the conceptual level of the retrieval word is made to match the currently set conceptual level of the synonym/concept dictionary 8. It is determined in step S312 whether the content of the counter is equal to or larger than a predetermined value max. If the counter content is equal to or larger than the predetermined value max (YES in step S312), the flow advances to step S319. In step S319, a retrieval result notification unit 12 outputs a message representing that no corresponding image data has been retrieved. When the counter content is smaller than the predetermined value max (NO in step S312), the flow advances to step S313. In step S313, full text retrieval/keyword retrieval based on the retrieval word is executed. It is determined in step S314 whether image data to be retrieved is present. If no image data to be retrieved is present (NO in step S314), the flow advances to step S322. If image data to be retrieved is present (YES in step S314), the flow advances to step S315. It is determined in step S315 whether the retrieved image data include representative image data. If representative image data are included (YES in step S315), the flow advances to step S323. In step S323, an optimum image feature weight for the retrieval word is extracted by looking up the synonym/concept dictionary 8. Taking the extracted image feature weight of the retrieval word into consideration, the common image feature amount of the representative image data or a significant image feature amount based on the principle of majority rule is extracted. If no representative image data are included (NO in step S315), the flow advances to step S316. In step S316, an optimum image feature weight for the retrieval word is extracted by looking up the synonym/concept dictionary 8. Taking the extracted image feature weight of the retrieval word into consideration, the common image feature amount of the retrieved image data or a significant image feature amount based on the principle of majority rule is extracted. In step S317, image data similar to the retrieved image data are retrieved on the basis of the extracted image feature amount by looking up the image feature index 9. In step S318, the representative image data are excluded from the retrieved similar image data. The similar image data retrieved in step S317 and the image data retrieved in step S313 are ANDed, and the resultant image data are output to the retrieval result notification unit 12 as a retrieval result. The retrieved image data and similar image data are displayed as thumbnail images. If it is determined in step S314 that no image data are to be retrieved, in step S322, a synonym of conceptual level next to the retrieval word is acquired by looking up the synonym/concept dictionary 8, and set as a new retrieval condition. If there are a plurality of synonyms, the OR of these synonyms is set as a new retrieval condition. In step S321, the counter content is incremented by one, and the flow returns to step S312. If image data to be retrieved by full text retrieval/keyword retrieval based on a retrieval word "automobile" are not present, full text retrieval/keyword retrieval is performed using, as a retrieval word, each of "wheeled vehicle", "car", and "mobile unit" of conceptual levels next to that of "automobile" in the synonym/concept dictionary 8. If no image data have been retrieved yet, full text retrieval/keyword retrieval is performed again using, as a retrieval word, each of "sport car" and "vehicle" of conceptual levels next to that of "automobile". If no image data have been retrieved yet, a word having no generic/specific concept relationship, or a word separated from the conceptual level of the retrieval word by, e.g., three levels is acquired. The predetermined value max is determined on the basis of the number of times the processing is repeated in steps S322 to S320 and can be arbitrarily set from the user interface 1. The user can infer the precision of the retrieval result on the basis of the predetermined value max. Normally, as the predetermined value max becomes smaller, a more precise retrieval result is obtained. In addition, a retrieval result corresponding to each conceptual level can be obtained. Refer back to the flow chart shown in Fig. 16. When retrieval processing is executed in step S213, the retrieval result is displayed on the retrieval result notification unit 12 in step S214. In step S215, an instruction for the retrieval result is received from the user. If the user instructs to set a new retrieval word to narrow down the retrieval result, retrieval instruction for further narrowing down the current retrieval result is set from the user interface 1 in step S221. The flow returns to step S212 to input a new retrieval word. In this case, the current retrieval result is held. The retrieval result based on the newly input retrieval word and the held retrieval result are ANDed to narrow down the retrieval result. If the user instructs to end retrieval processing, processing is ended. If the user instructs to perform retrieval again, the retrieval result is cleared in step S222, and the flow returns to step S211. If the user selects one of image data (thumbnail image data) displayed as the retrieval result to execute processing for the desired image data, it is determined in step S216 whether the processing for the selected image data is a request for displaying the detail of the image data (full-scale image data). If display of the detail of the selected image data is requested (YES in step S216), the flow advances to step S217 to display the detail (full-scale image data) of the selected image data (thumbnail image data). In step S218, the retrieval word is registered in the full text retrieval index/keyword index 11 in correspondence with the image data as a new description or keyword of the selected image data. When the detail of the selected image data is to be displayed, the image data is handled as image data corresponding to the retrieval word. The retrieval word is automatically additionally registered in the full text retrieval index/keyword index 11 as the description or keyword of the image data. That is, the image processing apparatus has a function of automatically additionally registering an input retrieval word in the full text retrieval index/keyword index 11 as a description or keyword. Consequently, the descriptions or keywords of image data to be retrieved except representative image data in initial installation are also registered in the full text retrieval index/keyword index 11, so the contents of the full text retrieval index/keyword index 11 can be enriched. If retrieval using the same retrieval word is to be performed, highspeed retrieval can be performed because the retrieval word has already been registered in the full text retrieval index/keyword index 11. If display of the detail of the selected image data is not requested in step S216 (NO in step S216), the flow advances to step S219 to retrieve image data similar to the selected image data. In step S220, display of current thumbnail image data is updated in accordance with the retrieved similar image data. When image data similar to the selected image data are to be retrieved, similar image data are retrieved on the basis of the image feature amount of the image data by looking up the image feature index 9. When similar image data close to the image data desired by the user are obtained by this processing, retrieval narrowing down in step S221 becomes more effective. As described above, according to the second embodiment, image data desired by the user can be easily retrieved without adding any description or keyword for managing image data. <Third Embodiment> The third embodiment is an application example of retrieval processing shown in Fig. 17 in the second embodiment. In the second embodiment, retrieval processing is performed using the conceptual level of the synonym/concept dictionary 8. However, in the third embodiment, the conceptual distance of a synonym/concept dictionary 8 is used to perform retrieval processing. Details of retrieval processing executed in the third embodiment will be described below with reference to Fig. 18. Fig. 18 is a flow chart showing details of retrieval processing executed in the third embodiment of the present invention. In step S331, the conceptual distance between the concept represented by a retrieval word and the currently set concept of the synonym/concept dictionary 8 is set at 0, and the concept of the retrieval word is made to match the currently set concept of the synonym/concept dictionary 8. It is determined in step S332 whether the conceptual distance exceeds the limit for continuing retrieval. If the conceptual distance exceeds the limit (YES in step S332), the flow advances to step S339. In step S339, a retrieval result notification unit 12 outputs a message representing that no corresponding image data has been retrieved. If the conceptual distance does not exceed the limit (NO in step S332), the flow advances to step S333. In step S333, full text retrieval/keyword retrieval based on the retrieval word is executed. It is determined in step S334 whether image data to be retrieved are present. If no image data are to be retrieved (NO in step S334), the flow advances to step S342. If image data to be retrieved are present (YES in step S334), the flow advances to step S335. It is determined in step S335 whether retrieved image data include representative image data. If representative image data are included (YES in step S335), the flow advances to step S343. In step S343, an optimum image feature weight for the retrieval word is extracted by looking up the synonym/concept dictionary 8. Taking the extracted image feature weight of the retrieval word into consideration, the common image feature amount of the representative image data or a significant image feature amount based on the principle of majority rule is extracted. If no representative image data are included (NO in step S335), the flow advances to step S336. In step S336, an optimum image feature weight for the retrieval word is extracted by looking up the synonym/concept dictionary 8. Taking the extracted image feature weight of the retrieval word into consideration, the common image feature amount of the retrieved image data or a significant image feature amount based on the principle of majority rule is extracted. When the retrieval word contains specific information representing an image feature amount, the image feature amount is also extracted as an image feature amount. In step S337, image data similar to the retrieved image data are retrieved on the basis of the extracted image feature amount by looking up an image feature index 9. In step S338, the representative image data are excluded from the retrieved similar image data. The similar image data retrieved in step S337 and the image data retrieved in step S333 are ANDed, and the resultant image data are output to the retrieval result notification unit 12 as a retrieval result. The retrieved image data and similar image data are displayed as thumbnail images. If no image data are to be retrieved in step S334, the flow advances to step S342 to acquire a synonym having a concept next to the concept of the retrieval word by looking up the synonym/concept dictionary 8. It is determined in step S341 whether a synonym has been acquired. If no synonym has been acquired (NO in step S341), processing is ended. If a synonym has been acquired (YES in step S341), the flow advances to step S340. In step S340, a synonym group having generic and specific concepts closest to the retrieval word is acquired while calculating the sum of conceptual distances within a predetermined conceptual distance from the concept of the retrieval word. The OR of the acquired synonyms is set as a new retrieval condition, and the flow returns to step S332. For example, when image data to be retrieved by full text retrieval/keyword retrieval using a retrieval word "automobile" is not present, "car" which is closest to "automobile" and has a conceptual distance of 3 from "automobile" in the synonym/concept dictionary 8 is set at a retrieval word, and full text retrieval/keyword retrieval is performed again. If no image data have been retrieved yet, "mobile unit" which is close to "automobile" next to "car" and has a conceptual distance of 4 is used as a retrieval word, and full text retrieval/keyword retrieval is performed again. If no image data have been retrieved yet, the OR of "wheeled vehicle" and "sport car" both of which have a conceptual distance of 5 from "automobile" is set as a retrieval word, and full text retrieval/keyword retrieval is performed again. If no image data have been retrieved yet, a word having no generic/specific concept relationship, a word for which the sum of the conceptual distance from the concept of the retrieval word is limited, or three words having similar conceptual distance sums are acquired. If no image data have been retrieved under the retrieval condition which has been newly set by processing in steps S342 to S340, synonyms having farther generic and specific concepts are acquired, and processing in steps S342 to S340 is repeated. The retrieval precision may lower or system runaway may occur depending on the number of times the retrieval is repeated. For this reason, in step S332, the conceptual distance limit for limiting the number of times the retrieval is repeated in steps S342 to S340 is set. Not the conceptual distance but the number of times the retrieval is repeated in steps S342 to S340 may be limited. As described above, according to the third embodiment, image data desired by the user can be easily retrieved without adding any description or keyword for managing image data. The present invention may be applied to a system constituted by a plurality of devices (e.g., a host computer, an interface device, a reader, a printer, and the like) or an apparatus comprising a single device (e.g., a copying machine, a facsimile apparatus, or the like) The object of the present invention is realized even by supplying a storage medium storing software program codes for realizing the functions of the above-described embodiments to a system or an apparatus, and causing the computer (or a CPU or an MPU) of the system or the apparatus to read out and execute the program codes stored in the storage medium. In this case, the program codes read out from the storage medium realize the functions of the above-described embodiments by themselves, and the storage medium storing the program codes constitutes the present invention. As a storage medium for supplying the program codes, a floppy disk, a hard disk, an optical disk, a magnetooptical disk, a CD-ROM, a CD-R, a magnetic tape, a nonvolatile memory card, a ROM, or the like can be used. The functions of the above-described embodiments are realized not only when the readout program codes are executed by the computer but also when the OS (Operating System) running on the computer performs part or all of actual processing on the basis of the instructions of the program codes, as a matter of course. The functions of the above-described embodiments are also realized when the program codes read out from the storage medium are written in the memory of a function expansion board inserted into the computer or a function expansion unit connected to the computer, and the CPU of the function expansion board or function expansion unit performs part or all of actual processing on the basis of the instructions of the program codes, as a matter of course. When the first embodiment of the present invention is applied to the storage medium, program codes corresponding to the above-described flow charts are stored in the storage medium. That is, modules shown in the memory map of Fig. 19 are stored in the storage medium. More specifically, at least program codes for a "first input module", an "analysis module", an "output module", a "second input module", and a "retrieval module" may be stored in the storage medium. The "first module" inputs the first retrieval condition for retrieving desired image data. The "analysis module" analyzes the input retrieval condition. The "output module" outputs a question on the basis of the analysis result to prompt the user to inputs the second retrieval condition different from the first retrieval condition. The "second input module" inputs the second retrieval condition in accordance with the output question. The "retrieval module" retrieves image data on the basis of the first and second retrieval conditions. When the second embodiment of the present invention is applied to the storage medium, program codes corresponding to the above-described flow charts are stored in the storage medium. That is, modules shown in the memory map of Fig. 20 are stored in the storage medium. More specifically, at least program codes corresponding to an "extraction module", a "first storage module", a "second storage module", an "input module", a "first retrieval module", an "execution module", an "acquisition module", a "second retrieval module", and a "display module" may be stored in the storage medium. The "extraction module" extracts the image feature amount of image data. The "first storage module" stores correspondence between the extracted image feature amount and the image data in the image feature amount index. When retrieval information used to retrieve image data is added to the image data, the "second storage module" stores correspondence between the retrieval information and the image data in the retrieval information index. The "input module" inputs a retrieval condition for retrieving desired image data. The "first retrieval module" retrieves image data having retrieval information corresponding to the input retrieval condition by looking up the retrieval information index. The "execution module" sets a new retrieval condition in accordance with the retrieval result and repeatedly executes retrieval. The "acquisition module" monitors the number of times the retrieval is executed and acquires retrieved image data in accordance with the monitor result. The "second retrieval module" retrieves image data similar to the acquired image data on the basis of the image feature amount of the image data by looking up the image feature amount index. The "display module" displays image data corresponding to the retrieval condition on the basis of the acquired image data and retrieved image data. When the third embodiment of the present invention is applied to the storage medium, program codes corresponding to the above-described flow charts are stored in the storage medium. That is, modules shown in the memory map of Fig. 21 are stored in the storage medium. More specifically, at least program codes corresponding to an "extraction module", a "first storage module", a "second storage module", an "input module", a "first retrieval module", a "second retrieval module", and a "display module" may be stored in the storage medium. The "extraction module" extracts the image feature amount of image data. The "first storage module" stores correspondence between the extracted image feature amount and the image data in the image feature amount index. When retrieval information used to retrieve image data is added to the image data, the "second storage module" stores correspondence between the retrieval information and the image data in the retrieval information index. The "input module" inputs a retrieval condition for retrieving desired image data. The "first retrieval module" retrieves image data having retrieval information corresponding to the input retrieval condition by looking up the retrieval information index. The "second retrieval module" retrieves image data similar to the retrieved image data on the basis of the image feature amount of the image data by looking up the image feature amount index. The "display module" displays image data corresponding to the retrieval condition on the basis of the retrieved image data and image data retrieved by the second retrieval means. As many apparently widely different embodiments of the present invention can be made without departing from the scope thereof, it is to be understood that the invention is not limited to the specific embodiments thereof except as defined in the appended claims.

Claims: An image processing apparatus for managing image data, comprising: first input processing means (1) for receiving input of text defining a first retrieval condition for retrieving desired image data; analysis means (6, 8, 10) for analysing the text by referring to a dictionary to extract a retrieval object name identifying an object represented in the desired image data and to extract from the text an outer appearance feature which is descriptive of the object; said dictionary defining for each of a set of names a respective n dimensional image feature amount; output means (12) for outputting a question based on an analysis result from said analysis means to prompt a user to input a second retrieval condition different from the first retrieval condition; second input processing means (1) for receiving input of the second retrieval condition in response to the question output by said output means; and retrieval means (10a, 9) for retrieving image data based on the first and second retrieval conditions by searching an index of the image data containing a reference set of image feature amounts using search parameters derived from the image feature amount defined by the dictionary in respect of the retrieval object name; characterised by : said dictionary further defining for each name an n dimensional image feature weight representing the relative importance of components of the image feature amount as parameters for searching, said second retrieval condition defining user input data on the effectiveness as parameters for searching of the n components of the image feature amount corresponding to the retrieval object name; and comprising calculating means for calculating the search parameters by weighting the n components of the image feature amount with the image feature weight modified in accordance with the outer appearance feature and the second retrieval condition. The apparatus according to claim 1 wherein, when the outer appearance feature of the object identified by the retrieval object name has not been acquired by said analysis means, said output means is operable to output a question to prompt the user to input a user generated outer appearance feature of the object identified by the retrieval object name. The apparatus according to claim 2 wherein, said second input means is operable to input the user generated outer appearance feature of the object identified by the retrieval object name in accordance with the question. The apparatus according to claim 3, wherein the calculation means is operable to calculate the search parameters using as said outer appearance feature the user generated outer- appearance feature input by said second input means. The apparatus according to claim 1, wherein when a retrieval object name corresponding to the text has not been extracted by the analysis means, said output means is operable to output a question to prompt the user to input a word having a concept close to the object represented in the desired image data. The apparatus according to claim 5, wherein when the question has been output by said output means, said second input means is operable to input the word having a concept close to the object and an outer appearance feature of the object. The apparatus according to claim 6, wherein when the word having a concept close to the object represented by the retrieval object name and the user generated outer appearance feature have been input by said second input means, the calculation means is operable to calculate the search parameters based on the word and the user generated outer appearance feature. The apparatus according to claim 6, wherein when the word has been input-by said second input means, the retrieval object name is registered in said dictionary on the basis of a conceptual level of the word. A method of controlling an image processing apparatus for managing image data, comprising: a first input step (S202) of receiving input of text defining a first retrieval condition for retrieving desired image data; an analysis step (S301) of analysing the text by referring to a dictionary defining for each of a set of names a respective n dimensional image feature amount to extract (S301) a retrieval object name identifying an object represented in the desired image data and to extract (S301) from the text an outer appearance feature which is descriptive of the object; an output step (S305) outputting a question based on an analysis result from said analysis step to prompt a user to input a second retrieval condition different from the first retrieval condition; a second input step (S412) of receiving input of the second retrieval condition in response to the question output by the first output step; and a retrieval step (S306) of retrieving image data based on the first and second retrieval conditions by searching an index of the image data containing a reference set of image feature amounts using search parameters derived from the image feature amount defined by the dictionary in respect of the retrieval object name; characterised by : said dictionary further defining for each name an n dimensional image feature weight representing the relative importance of components of the image feature amount as parameters for searching, said second retrieval condition defining user input data on the effectiveness as parameters for searching of the n components of the image feature amount corresponding to the retrieval object name and comprising calculating (S406, S411) the search parameters by weighting the n components of the image feature amount with the image feature weight modified in accordance with the outer appearance feature and the second retrieval condition. The method of claim 8 wherein, when the outer appearance feature of the object identified by the retrieval object name has not been acquired by said analysis means, said output step outputs a question to prompt the user to input a user generated outer appearance feature of the object identified by the retrieval object name. The method according to claim 10 wherein, said second input step inputs the user generated outer appearance feature of the object identified by the retrieval object name in accordance with the question. The method according to claim 11, wherein the calculation step calculates the search parameters using as said outer appearance feature the user generated outer appearance feature input by said second input step. The method according to claim 9, wherein when a retrieval object name corresponding to the text has not been extracted by the analysis step, said output step outputs (S309) a question to prompt the user to input a word having a concept close to the object represented in the desired image data. The method according to claim 13, wherein when the question has been output by said output step, said second input step inputs the word having a concept close to the object and an outer appearance feature of the object. The method according to claim 14, wherein when the word having a concept close to the object represented by the retrieval object name and the user generated outer appearance feature have been input by said second input step, the calculation step calculates the search parameters based on the word and the user generated outer appearance feature. The method according to claim 14, wherein when the word has been input by said second input step, the retrieval object name is registered in said dictionary on the basis of a conceptual level of the word. A storage medium storing processor implementable instructions for instructing a processor to carry out all of the steps of a method as claimed in any one of claims 9 to 16.

Inventors: SHIIYAMA HIROTAKA, SHIIYAMA, HIROTAKA
Applicants: CANON KK, CANON KABUSHIKI KAISHA
Application year: 1998
Main classification: G06F17
IPC codes: G06F17