TWI297842B - Method and apparatus for content representation and retrieval in concept model space - Google Patents
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- G06F16/783—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
- G06F16/7837—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using objects detected or recognised in the video content
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Description
1297842 五、發明說明(1) 、【發明所屬之技術領域】. 本發明係關於一種利用模型向量以編索引多媒體文 I件,尤其是關於一種產生模型向量表示的方法及裝置,用 以聯i# (associating)模型向量及多媒體文件以提供索 引,且利用模型向量搜尋、分類及群集多媒體文件。本發 明亦關於利用模型向量於資訊探索、個人化多媒體内容及 查詢多媒體資·訊儲存庫等的目的。 、【先前技術】 數位資訊以影視(video)、影像(image)、文字及其他 多媒體文件形式的成長量,使得索引、搜尋、分類及組織 |文件需要更有效的方法。近來在内容分析、特徵抽取及刀 i類上的發展係改進對多媒體文件有效地搜尋及過濾的能 力。然而,對於如顏色、紋理、形狀、動作等可自動自多 |媒體内容抽取出的低階特徵描述,以及如物件、事件、景 |物及人物等對多媒體系統使用者具有意義的語意 semantic)描述之間,仍存在著明顯的差距。 多媒體索引的問題可藉由一些需要手動、半自動或全 自動處理技術說明。其一技術係利用註解或編目工具 (cataloging tool)以允許人類手動地歸屬(ascribe)標號 (label)、類型(category)或描述(descr i pt i on)給文件。 I例如,Μ· Naphade,C.-Y· Lin, J.R. Smith, Β· Tseng 及3.3&311等作者於20 02年1月在加州聖荷西的13&178?1£
4IBM03100TW.ptd 第6頁 1297842 五、發明說明(2) _
Symposium on Electronic Imaging: Science and
Tech no 1ogy-Storage & Retrieva1 for I mage and Video Databases X,發表"Learning to Annotate Video Data bases"的文章,其描述一允許指定(assign)標號到影 !視鏡頭的影視註解工具。作者們亦教導依據主動學習 (active learning)指定標號的半自動方法。全自動技術 亦屬可行。例如,.M. Naphade, S. Basu及 J. R. Smith於 2002年 5月在 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-20 02 )發表"A Statistical Modeling Approach to Content-based Video Retrieval”中,依據低階視覺特徵 i之統計模型’自動指定標號到影視内容的方法。自動標號 j技術對於允許依據自動指定標號的影視搜尋是有用的。然 而,索引係受限於一小字彙的匹配值(matching vaiue),' I使得假如使用者輸入搜尋詞而無法與標號詞之其一匹配, 則此次搜尋並未發現任何目標多媒體文件α 雖然自動系統改進指定標號、類型及描述到多媒體文 件的能力,然而仍需要能影響這些描述的新技術,以利 這些描述提供更有意義的方式以索引、搜尋、分類及群 文件。再者,系統應考慮自動系統的不確定性及可靠度了 以及任何指定到多媒體文件的標號、類型或描述的關聯 I性,以提供有效的索引。 第7頁 4IBM03100TW.ptd 1297842 五、發明說明(5) 分類器自以下辭彙:{ Π車”,"船”,π火車"}指定辭彙實 :豊,藉由檢測這些概念是否描述於一多媒體文件。檢測問 題可視為一組二元分類器,其係藉由指定一反映出每一概 念存在之確定性的計分,檢測每一概念的存在或缺少 (pre sence or absence)。例如,系統可給予”車” 〇 · 75的 計分,其可解釋成指定為”車’’標號的信賴度為75%的意 思。另一方面,系統可給予π火車” 0 · 2 5的計分,其可解釋 成指定為"火車π標號的信賴度為25%的意思。大體上系統 導致這些多個檢測器的計分,且模型向量以單一表示掏取 這些计分,然後其可作為多媒體文件的索引。 圖2係繪示對一多媒體文件或一查詢產生一模型向量 的程序。多媒體文件(2 0 0 )先以複數個檢測器(2 0 1 )操作, 且計分關於每一檢測器其下的概念。檢測器本身可能對應 至一固定辭彙(2 0 4 )或一組固定的類型、物件、事件、景 物或人物。例如,國會圖書館圖形資料詞庫(L i b r a r y 〇 f Congress Thesaurus of Graphical Material(TGM))提供 一組類型供編目相片或其他形式的圖形文件。檢測器可建 立及使用,使得每一檢測器對應TGM類型的其中一個。語 意(2 0 4 )的概念亦可為同屬:(generic)、特定(specific)或 摘要(a b s ΐ r a c t )。例如,一概念可對應一同屬實體’ 如π顯示橋的景物u。此外,一概念可對應一特定實體’ 如”顯示金門大橋的景物π。最後,一概念可對應一摘要實 體,如π現代文明”。於訓練(2 0 5 )時,標號多媒體文件的
4IBM03100TW.ptd 第10頁 1297842 五、發明說明(10) 間(discrete space)或二元值空間(binary一valued space)。例如,於對映階段( 304)藉由定限來自檢測器的 |信賴度計分(3 0 5 ),可產生二元模型向量係指示每一概念 是否存在或缺少於多媒體文件(3〇〇)中。 大體而言,對映(3 0 4 )可導致由個別概念或檢測器 (3 0 1 - 3 0 3 )至模型向量(3 0 6 )之個別維度各式的特定對映。 |於某些例子中,例如序連計分(3 〇 5 )的對映(3 0 4 ),可產生 1概念對模型向量維度的一對一對映。然而,於其他例子 中,可產生多對一的對映,以減少模型向量(3 〇 6 )相關於 |原概念空間的維數。於其他例子中,對映可為一對多或多 1對多,以允許模型向量(306 )某程度的冗餘。 圖4顯示依據檢測器計分所產生的模型向量的例子。 I對一固定辭彙= { C i t y s c a p en ’ u F a c e," I n d ο 〇 r s ”,,l L a n d s c a p e"," Μ ο η ologue, Outdoors丨丨,Ir P6οp 1 efT » ” Text-Overlay丨r,}之 ‘計分組的檢測器結果(4 〇 〇 ),藉由對映每一檢測器計分 (4 0 0 )至多維模型向量(4 0 1 )之一獨特維度,產生模型向量 (4 0 1 )。於此例中,” c i t y s c a p e"的計分為〇 · 3 5係對映至模 |型向量的第一維度。"Face"的計分為〇 · 87係對映至模型向 |量的第二雄度,以此類推。為助於模型向量與對應至不同 多媒體文件之匹配,可利用檢測器一致的對映至模型向量 |維度。相似地,對較大辭彙的範例檢測器計分(4 〇 2 )可對
4IBM03100TW.ptd 第15頁 1297842 五、發坍說明(π) 映至模型向量維度(4 0 3 )。於此例中,"An i ma 1"的計分係 對映至模型向量的第一維度。” B e a c h"的計分係對映至模 型向量的第二維度,以此類推。 圖5顯示利用模型向量索引多媒體文件的程序。首 先,_多媒體文件( 50 0 )的收集係於模型向量產生程序 (501 )中被分析,以產生一組μ個的模型向量(5〇2)。模型 向量產生程序(501)可利用一固定辭彙(5〇5)及對應之檢測 器組檢測過所有多媒體文件(50 0 ),以允許產生模型向量 一致性。再者,模型向量產生程序(5〇1)可利用一固定 組的參數,以相同的理由對所有多媒體文件(5〇〇)計分 (305)及對映( 304)。一旦產生模型向量(5〇2),其與相對 應的多媒體文件(5 0 0 )有關。例如,可利用資料庫關鍵值 表示相關性,其係陳述每一模型向量(5 〇 2 )與每一 ·多媒體 文件(500 )之主鍵-外鍵關係(primary key —fc)]re ign key relationship)。此外,可藉由聯結每一模型向量(5〇2)與 表示栢對映多媒體文件(5 〇 〇 )位址的一媒體定位符(med丄a locator),來表示聯結。此外,亦可利用獨特辨識每一多 媒體文件(5 0 0 )的辨識符以表示與每一模型向量(5 〇 2 )的關 係。亦可直接將模塑向量( 502 )與每一多媒體文件(5〇〇)聯 結,其係藉由表示模塑向量之值於每一多媒體文件(5 〇 〇 ) 之標頭(header)或元資料攔(metadata field),或藉由水 印或其他持續聯結方法’持續地聯結模型向量之值與每一 多媒體文件(500) 〇
I1H 4IBM031Q0TW.ptd 第16頁 1297842 五、發明說明(12)
一旦模型向量產生且表示出其與多媒體文件的聯結, 即建立允許存取( 504)多媒體文件(5〇〇)的索引,其係基於 模型向量(502 )之值°此索引可允許近似式存取 (proximity-base access),例如相似搜尋(similarity-searching)或最近相鄰搜尋(nearest neighbor s earch i ng )。於此例中,存取係藉由供應一查詢模型向量 (query model vector)及相似模型向量或自索引中找到的 固定尺寸組的最近目標模型向量。索引可支援範圍存取, 其中供應一查詢模型向量,且於索引中找到與查詢;j:莫& @ 量於一固定距離内之所有目標模型向量。 圖6顯示利用模型向量之查詢程序。模型向量之|% 與使用者查詢匹配,以檢索多媒體文件。使用者(6 〇 〇 出一查詢(6 0 1 )至多媒體文件搜尋系統(6 0 9 )。查詢可/ 使用者提供的範例模型向量形式。搜尋介面可選擇,〖生地大^ 許使用者著作出(author)查詢模型向量,例如,藉提供_ 介面以允許使用者辨識與查詢相關聯之語意概余, — •丹·夺曰 計分以建構模型向量表示。此外,搜尋介面可選擇性 現多媒體文件給使用者,以允許使用者選擇何者多媒1呈 件與查詢相關。然後系統可利用為查詢預先計算=聯,= 型向量,或於查詢時間產生模型向量。此外,:%^ _ 用者提供範例多媒體文件形式,其中可利用模型向量^ 程序分析查詢多媒體文件。以產生查詢模型向量。里產生
1297842 五、發明說明(13)
一旦查詢模型向量為可用時,其於步驟(6 0 2 )與儲存 的模禮向量值(6 0 6 )匹配。此匹配程序可涉及利用余引結 構以辨識目標模型向量配對(m a t c h e s)。此匹配可涉及如 上所述之相似性搜尋、最近相鄰搜尋或範圍查詢。此匹配 程序〔602 )產生一匹配清單(603),其辨識出匹配查詢模型 向量之儲存的模型向量。然後選擇性地於步驟(6 〇 4 )中計 分匹配清單(6 0 3 )。匹配計分可依據利用模型向量值的測 度空間什鼻而決疋。例如,考慮一單一查詢模型向量,匹 配計分可依據利用一.距離函數,如歐式距離(Eucl idean distance)或曼哈頓距離(Manhattan distance),於多維 模型向量空間鄰近量測。此外,匹配程序將可僅利用模型 向量的某些維度。·例如,考慮於(4 〇 〇 - 4 0 1 )之模型向量, 假如使用者僅對’’ human-related,,概念有興趣,於此例 中,則可選擇性使用維度2(" face,,)及維度7(” peopie”)。 在提供多個查詢模型向量的例子中,可藉由結合自獨立模 型向里之距離的計分而得到匹配計分。其他的選擇亦為可 月b ’如什异查詢模型向量的質心(c e n * r 〇丨^ ),及利用質心 模型向量做為查詢。 '
然後選擇性地於步驟(6 〇 5 )評級 (r a n k)計分之匹配清 單’例如將隶佳匹配移到清單的最上面。然後選擇性地於 步驟( 6 0 6 )刪截評級清單,例如保留最佳匹配的前十名於 清單上。然後將結果(6 〇 7 )提供回給使用者。選擇性地,
1297842 ——_________ 五、發11 月說明(14) $尋系統可自多媒體文件儲存庫(6丨〇 )中檢索該些與結果 清單〔6 0 7 )中之模型向量有關的多媒體文件,且呈現文件 給使用者。 | 一旦結果呈現給使用者,使用者可精化(ref ine)搜 尋,例如藉利用關聯性的回饋技術自結果清單(6 〇 7 )中辨 識正確及負面範例(posit iveand negative examples)。 搜尋系統(6 〇 9 )可依據模型向量計分利用與查詢處理關聯 的資訊檢索匹配。 當模型向量可用以檢索多媒體文件時,其亦可用以群 集及分類多媒體文件。例如,模型向量可於多維測度空間 被分析’以利用各式技術如凝聚式群集(aggl〇mera*t;ive clustering)來辨識群集。模型向量亦可利用各種指導學 習方法,如利用判別或產生的模型,加以分類。範例辨識 符包含支掩向量機及高斯混合模型。其他技術如主動學習 及激增(active learning and boost: i ng)亦可以分類為目 的應用至模型向量值。
4IBM03100TW.ptd 第19頁 1297842 五、發明說明(15) 圖7顯示使用模型向量的多媒體文件改編σ模型向量 可用以過濾(f i Iter)、彙總(summarize)或個人化 (personalize)來自一多媒體儲存庫的多媒體文件或資 丨訊。、一使用者(700 )發出一要求(708 )至多媒體資訊系統。 |要求於步驟(7 0 1 )中被處理。此要求可含有一特定使用者 查詢’例如於(601)中,使用者供應範例模型向量或多媒 體文件’或辨識語意概念。此外,此要求亦可為登錄 (login)形式,其中一設定擋(profile)及較偏好^訊早 已儲存供使用者備用。於此例中,於步驟(7〇2)可選擇性 地檢查使用者的偏好資訊。偏好資訊亦可以範例模型向 量、多媒體文件或經辨識的語意概念的形式儲存。'然後, 可聚集及處理使用者查詢及使用者偏好資訊,以產^查詢 模型向量,其係後續用以匹配及檢索儲存的模型向量一 5a (7 0 4 ),然後於步驟(7 〇 6 )用以作為索引以選擇性地自多 體儲存庫(7 0 5 )檢索多媒體文件。如此係基於模型向量^ 、 |值,提供多媒體文件的過濾。 、〇里 選擇性地,與多媒體文件聯結的模型向量可用以與杳 詢模型向量組合,以於步驟( 707 )改編多媒體文件内容、。一 此改編可根據使用者對特定的查詢偏好,個人化多媒&體 件的内容。例如,檢索的多媒體内容,如”新聞,,影視~ ("news’1 video’),可被處理以只抽取出”運動”的=斷 (n sports" segment)。此外,此改編可彙總内容,例如 用壓縮π非運動”(” non^sports)片斷及自”運動”片斷抽取
4IBM03100TW.ptd 1297842 五、發明說明(16) 出重要部分(highl ight )。 本發明以較佳實施例描述,但熟習此領域的人士可有 其他未脫離本發明所揭示之精神下所完成之等效改變或修 飾,均應包含在下述之申請專利範圍之範疇。
4IBM03100TW.ptd 第21頁 1297842 圖式簡拳說明 i五、【圖式簡單說明】 本發明配合參考附圖將詳細的描述,其中: 圓1係一多媒體資訊檢索系統,其中查詢處理器利用 I模型句量索引做搜尋; 圖2係對給予一辭彙及一組受訓檢測器之多媒體文件 |產生一模型向量; 圖3你對多媒體文件產生模型向量之檢測、計分及對 I映程序; 圖4係以檢測計分產生模型向量之範例; 圖5係利用模型向量索引多媒體文件的程序; 圖6係利用模型向量之查詢程序;以及 .圖7係利用模型向量的多媒體文件改編。 圖式元件符號說明 100 使 用 者 介 面 101 查 詢 處 理 器 102 檢 索 引 擎 103 多 媒 體 儲 存庫 104 索 引 值 200 多 媒 體 文 件 201 檢 測 器 202 對 映 程 序 203 模 型 向 量 204 辭 彙 205 訓 練 30 0 多 媒 體 文 件 301 檢 測 器 302 檢 測 器 303 檢 測 器 304 對 映 305 計 分 306 模 型 向 量 »
4IBM03100TW.ptd 第22頁 1297842
圖式簡 阜說明 400 檢測 器 計 分 401 模 型 向 量 402 檢測 器 計 分 403 模 型 向 量 500 多媒 體 文 件 501 模 型 向 量 產 生程 序 502 模型 向 量 50 3 產 生 索 引 504 存取 505 辭 彙 50 6 參數 60 0 使用 者 601 查 詢 602 匹配 603 匹 配 清 單 604 匹配 計 分 605 評 級 606 刪截 60 7 結 果 608 模型 向 量 60 9 多 媒 體 文 件 搜尋 系統 610 多媒 體 儲 存 庫 700 使用 者 701 處 理 使 用 者 要求 702 檢查 使 用 者 偏好 70 3 匹 配 模 型 向 量 704 模型 向 量 705 多 媒 體 儲 存 庫 706 檢索 多 媒 體 文件 707 改 編 内 容 708 要求 70 9 回 應 4IBM03100rrW.ptd 第23頁
Claims (1)
- —— 92131652 ί热 5, 23 年月曰 修正 1. 一種產生至少一模型向量以表示一多媒體文件而促進搜 尋及分類該文件以及群集該文件與其他多媒體文件之方 法,包含: 應用複數個概念檢測器於該多媒體文件,每一概念檢 測器係對應至一組固定辭彙實體、類型、物件、特徵、事 件、景物及人物之至少一概念; 對每一檢測器,計分該多媒體文件;以及 對映該計分至一多維空間,以產生至少一向量表示。/ 2. 如申請專利範圍第1項所述之方法,其中該複數個概念 檢測器對應類型、物件、事件、景物及人物之一固定辭 彙。 3. 如申請專利範圍第1項所述之方法,其中該文件包含多 個模態,如聲頻、視覺、文字及語音,且其中該概念檢測 器操作呈現於該多媒體文件之單一或多個模態。 4. 如申請專利範圍第1項所述之方法,其中該概念檢測器 操作抽取自該多媒體文件之文件特徵之内容式描述符。 5. 如申請專利範圍第1項所述之方法,其中該概念檢測器 操作與該多媒體文件有關之元資料。 6. 如申請專利範圍第1項所述之方法,其中該概念檢測器4IBM03100TW-替換頁-052306.ptc 第24頁 1297842 ^,5.23 _ 案號 92131652 _年月日__ 六、申請專利範圍 操作與該多媒體文件有關之上下文資訊。 7. 如申請專利範圍第1項所述之方法,其中該概念檢測器 操作與該多媒體文件有關之知識庫。 8. 如申請專利範圍第1項所述之方法,其中該檢測器搡作 對應至受訓之模型統計分類器。 9 ·如申請專利範圍第1項所述之方法,其中該計分依據於 該多媒體文件中檢测一概念的信賴度、一概念與該多媒體 文件之關聯性以及對該概念該檢測器之可靠度等的至少一 個。 1 0.如申請專利範圍第1項所述之方法,其中該對映係對應 至序連該計分、執行線性轉換、執行非線性轉換、執行量 化及利用定很使維數減少等之至少一個。 11.如申請專利範圍第1 0項所述之方法,其中該對映該計 分至模型向量維度可為一對一、一對多、多對一或多對 多。 1 2 ·如申請專利範圍第1項所述之方法,其中該複數個檢測 器對該多媒體文件的索引係固定的。4IBM03100TW-替換頁-052306.ptc 第25頁 '1297842 S5·5·2^ : _ 案號92131652_ 年 月 日 修正__ 六、申請專利範圍 1 3 ·如申請專利範圍第1項所述之方法,其中該計分及對映 方法對該多媒體文件的索引係固定的。 1 4. 一種利用模型向量索引多媒體文件而促進搜尋、分類 以及群集該文件的方法,包含·: 基於自複數個概念檢測器之輸入,對每一多媒體文件 產生一或多個模型向量,每一概念檢測器係對應至一組固 定辭彙實體、類型、物件、特徵、事件、景物及人物之至 少一概念; 聯結該模型向量與對應之多媒體文件;以及 建立一索引,以依據該相關的模型向量之值存取該多 媒體文件。 1 5.如申請專利範圍第1 4項所述之方法,其中該產生至少 一模型向量以表示一多媒體文件包含: 應用複數個概念檢測器於該多媒體文件,每一概念檢 測器係對應至一組固定辭彙實體、類型、物件、特徵、事 件、景物及人物之至少一概念; 對每一檢測器,計分該多媒體文件;以及 對映該計分至一多維空間,以產生至少一向量表示。 1 6 ·如申請專利範圍第1 5項所述之方法,其中該複數個檢 測器對該多媒體文件的索引係固定的。4IBM03100TW-替換頁-052306.ptc 第26頁 1297842 敗 5. 23 _案號92131652_年月日__ 六、申請專利範圍 1 7.如申請專利範圍第1 5項所述之方法,其中該計分及對 映方法對該多媒體文件的索引係固定的。 1 8.如申請專利範圍第1 4項所述之方法,其中該多個模型 向量係依據多個模態、特徵、描述符或模型的每一個對每 一多媒體文件產生。 1 9.如申請專利範圍第1 4項所述之方法,其中該聯結係依 據資料庫鍵值、媒體定位符或其它種形式的辨識符。 2 0.如申請專利範圍第1 4項所述之方法,其中,該索引依 據該模型向量值允許相似搜尋、最近相鄰存取或範圍搜 尋。 2 1. —種利用模型向量於多媒體文件應用的方法,包含: 產生至少一模型向量以表示每一多媒體文件,包含·· 應用複數個概念檢測器於該多媒體文件,每一概念 檢測器係對應至一組固定辭彙實體、類型、物件、特徵、 事件、景物及人物之至少一概念; 對每一檢測器,計分該多媒體文件;以及 對映該計分至一多維空間,以產生至少一向量表 示;以及 根據該至少一向量表示之值,執行至少一操作於該多 媒體文件。4IBM03100TW-替換頁-052306.ptc 第27頁 1297842 郎· 5· 23 _案號92131652_年月 日 修正__ 六、申請專利範圍 2 2 .如申請專利範圍第2 1項所述之方法,其中該至少一操 作包含利用該模型向量自一多媒體資訊儲存庫搜尋及檢索 文件。 2 3.如申請專利範圍第2 1項所述之方法,其中該至少一操 作包含過濾、彙總及個人化多媒體資訊等之至少一個。 2 4.如申請專利範圍第2 1項所述之方法,其中該至少一操 作包含資料採掘。 2 5.如申請專利範圍第21項所述之方法,其中該至少一操 作包含群集該文件。 2 6.如申請專利範圍第2 1項所述之方法,其中該至少一操 作包含分類該文件。 2 7. —種機器可讀之程式儲存裝置,有形地具體實現可由 該機器執行之指令之一程式,以執行一種產生表示一多媒 體文件之至少一模型向量而促進搜尋及分類該文件以及群 集該文件與其他多媒體文件之方法,該方法包含: 應用複數個概念檢測器於該多媒體文件,每一概念檢 測器係對應至一組固定辭彙實體、類型、物件、特徵、事 件、景物及人物之至少一概念;4IBM03100TW-替換頁-052306.ptc 第 28 頁 1297842 5.23 _ 案號92131652_年 月 曰 修正_ 六、申請專利範圍 對每一檢測器,計分該多媒體文件;以及 對映該計分至一多維空間,以產生至少一向量表示。 28. —種機器可讀之程式儲存裝置,有形地具體實現可由 該機器執行之指令之一程式,以執行一種利用模型向量於 多媒體文件應用而促進搜尋及分類該文件以及群集該文件 與其他多媒體文件之方法,其中該方法包含: 產生至少一模型向量以表示每一多媒體文件,包含: 應用複數個概念檢測器於該多媒體文件,每一概念 檢測器係對應至一組固定辭彙實體、類型、物件、特徵、 事件、景物及人物之至少一概念; 對每一概念檢測器,計分該多媒體文件;以及 對映該計分至一多維空間,以產生至少一向量表 示;以及 根據該至少一向量表示之值,執行至少一操作於該多 媒體文件。 2 9. —種利用模型向量於多媒體文件應用之系統,包含: 至少一模型向量產生元件,基於自複數個概念檢測器 之輸入,用以產生至少一模型向量以表示每一多媒體文件 ,每一概念檢測器係對應至一組固定辭彙實體、類型、物 件、特徵、事件、景物及人物之JL少' ^概念;以及 至少一文件處理元件,根據該至少一向量表示之值, 用以執行至少一操作於該多媒體文件而促進搜尋及分類該4IBM03100TW-替換頁-052306. ptc 第29頁 1297842' m. m __案號92131652_年月曰 修正__ 六、申請專利範圍 文件以及群集該文件與其他多媒體文件。 3 0 .如申請專利範圍第29項所述之系統,其中該至少一模 型向量產生元件包含: 至少一概念檢測應用元件,用以應用複數個概念檢測 器於該多媒體文件,每一概念檢測器係對應至一組固定辭 彙實體、類型、物件、特徵、事件、景物及人物之至少一 概念; 一計分元件,對每一檢測器用以計分該多媒體文件; 以及 一對映元件,用以對映該計分至一多維空間以產生至 少一向量表示; 根據該至少一向量表示之值,執行至少一操作於該多 媒體文件。4IBM031OOTW -替換頁-052306. p t c 第30頁
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