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", "tags": [], "relations": {}, "dateAdded": "2013-10-07T15:17:48Z", "dateModified": "2013-10-07T15:17:48Z" } }, { "key": "7W3SFFEK", "version": 2, "library": { "type": "group", "id": 220969, "name": "HCI Project", "links": { "alternate": { "href": "https://www.zotero.org/groups/hci_project", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/220969/items/7W3SFFEK", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/hci_project/items/7W3SFFEK", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1268205, "username": "elovero", "name": "Elizabeth Lovero", "links": { "alternate": { "href": "https://www.zotero.org/elovero", "type": "text/html" } } }, "creatorSummary": "Zhang et al.", "parsedDate": "2010", "numChildren": 2 }, "data": { "key": "7W3SFFEK", "version": 2, "itemType": "journalArticle", "title": "Classification Images for Search in Natural Images", "creators": [ { "creatorType": "author", "firstName": "S.", "lastName": "Zhang" }, { "creatorType": "author", "firstName": "C.", "lastName": "Abbey" }, { "creatorType": "author", "firstName": "M.", "lastName": "Eckstein" } ], "abstractNote": "", "publicationTitle": "Journal of Vision", "publisher": "", "place": "", "date": "2010", "volume": "10", "issue": "7", "section": "", "partNumber": "", "partTitle": "", "pages": "1355-1355", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1167/10.7.1355", "citationKey": "", "url": "http://utexas.summon.serialssolutions.com/link/0/eLvHCXMwY2BQsAA1s42TDc2SQNOayQaJpolp5hYpwMhPMTExSAPtGw5xNXcLM3PxtfBGKs3dhBiYUvNEGTTdXEOcPXRh1UR8AeTchXhwe9XMHEQDCWBFaSjGwALsJKeKM7CmASMLSAMLUHGgYeIMHBGWzpF-UT5uEK4QjKtXDN7MpFdYIg4sr8FxrWukZwAA47orcA", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "Journal Article", "tags": [], "collections": [], "relations": {}, "dateAdded": "2013-10-07T15:17:48Z", "dateModified": "2013-10-07T15:17:48Z" } }, { "key": "PW5KSDIP", "version": 2, "library": { "type": "group", "id": 220969, "name": "HCI Project", "links": { "alternate": { "href": "https://www.zotero.org/groups/hci_project", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/220969/items/PW5KSDIP", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/hci_project/items/PW5KSDIP", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1268205, "username": "elovero", "name": "Elizabeth Lovero", "links": { "alternate": { "href": "https://www.zotero.org/elovero", "type": "text/html" } } }, "creatorSummary": "Xinmei Tian et al.", "parsedDate": "2012", "numChildren": 2 }, "data": { "key": "PW5KSDIP", "version": 2, "itemType": "journalArticle", "title": "Query Difficulty Prediction for Web Image Search", "creators": [ { "creatorType": "author", "name": "Xinmei Tian" }, { "creatorType": "author", "name": "Yijuan Lu" }, { "creatorType": "author", "name": "Linjun Yang" } ], "abstractNote": "Image search plays an important role in our daily life. Given a query, the image search engine is to retrieve images related to it. However, different queries have different search difficulty levels. For some queries, they are easy to be retrieved (the search engine can return very good search results). While for others, they are difficult (the search results are very unsatisfactory). Thus, it is desirable to identify those \"difficult\" queries in order to handle them properly. Query difficulty prediction (QDP) is an attempt to predict the quality of the search result for a query over a given collection. QDP problem has been investigated for many years in text document retrieval, and its importance has been recognized in the information retrieval (IR) community. However, little effort has been conducted on the image query difficulty prediction problem for image search. Compared with QDP in document retrieval, QDP in image search is more challenging due to the noise of textual features and the well-known semantic gap of visual features. This paper aims to investigate the QDP problem in Web image search. A novel method is proposed to automatically predict the quality of image search results for an arbitrary query. This model is built based on a set of valuable features that are designed by exploring the visual characteristic of images in the search results. The experiments on two real image search datasets demonstrate the effectiveness of the proposed query difficulty prediction method. Two applications, including optimal image search engine selection and search results merging, are presented to show the promising applicability of QDP.", "publicationTitle": "IEEE Transactions on Multimedia", "publisher": "", "place": "", "date": "2012", "volume": "14", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "951-962", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1109/TMM.2011.2177647", "citationKey": "", "url": "http://utexas.summon.serialssolutions.com/link/0/eLvHCXMwTV09CwIxDC2i4CxoV_9Aj7bXz_m8IvgxnaJuba8ZBdH_j7lTwSmEDBne45FHCCFk7YYxu87CpGGtmXnUEazrEfxeKQ7D3XDX2nA2m4Pb_al5WJBJuS_JKbRds2XfZwAso2uRTIBFvpXIOSRQPkfukk5aGCwnW3wtwKMxlEVIo3uVM8isa5ti1EpFF8WKTNFQF0pmgMBiRLGl2JiS-cU31-NtHz7p4pdWz_HwqXq8KGr7yAsmK_4G2Ro11g", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "1520-9210", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "Journal Article", "tags": [ { "tag": "Coherence" }, { "tag": "Database languages" }, { "tag": "Feature extraction" }, { "tag": "Image retrieval" }, { "tag": "Predictive models" }, { "tag": "Search engines" }, { "tag": "Vectors" }, { "tag": "Visualization" }, { "tag": "image search quality" }, { "tag": "query difficulty prediction (QDP)" } ], "collections": [], "relations": {}, "dateAdded": "2013-10-07T15:17:48Z", "dateModified": "2013-10-07T15:17:48Z" } }, { "key": "WMD6NAT5", "version": 2, "library": { "type": "group", "id": 220969, "name": "HCI Project", "links": { "alternate": { "href": "https://www.zotero.org/groups/hci_project", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/220969/items/WMD6NAT5", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/hci_project/items/WMD6NAT5", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1268205, "username": "elovero", "name": "Elizabeth Lovero", "links": { "alternate": { "href": "https://www.zotero.org/elovero", "type": "text/html" } } }, "creatorSummary": "Notess", "parsedDate": "2013", "numChildren": 2 }, "data": { "key": "WMD6NAT5", "version": 2, "itemType": "journalArticle", "title": "Yahoo! Image Search", "creators": [ { "creatorType": "author", "firstName": "Greg R.", "lastName": "Notess" } ], "abstractNote": "", "publicationTitle": "Online Searcher", "publisher": "", "place": "", "date": "2013", "volume": "37", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "9", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "http://utexas.summon.serialssolutions.com/link/0/eLvHCXMwY2BQsAA1s42TDc2SQNOayQaJpolp5hYpwMhPMTExSAPtGw5xNXcLM3PxtfBGKs3dhBiYUvNEGWTdXEOcPXRBJWR8AeTMhXhjI2Mz0IXI5oZiDCzAjnGqOANrGjCCgDSw0BQHGiDOwBFh6RzpF-XjBuEKwbh6xeANTHqFJeLAMhocv7pGegYAgQYnUw", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "2324-9684", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "Journal Article", "tags": [ { "tag": "Information services" }, { "tag": "Information services industry" }, { "tag": "Service enhancement" } ], "collections": [], "relations": {}, "dateAdded": "2013-10-07T15:17:48Z", "dateModified": "2013-10-07T15:17:48Z" } }, { "key": "ICJX6EWU", "version": 2, "library": { "type": "group", "id": 220969, "name": "HCI Project", "links": { "alternate": { "href": "https://www.zotero.org/groups/hci_project", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/220969/items/ICJX6EWU", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/hci_project/items/ICJX6EWU", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1268205, "username": "elovero", "name": "Elizabeth Lovero", "links": { "alternate": { "href": "https://www.zotero.org/elovero", "type": "text/html" } } }, "creatorSummary": "M. Gokul Prasad et al.", "parsedDate": "2011", "numChildren": 2 }, "data": { "key": "ICJX6EWU", "version": 2, "itemType": "journalArticle", "title": "Semantic Web Image Search through Manual Annotation", "creators": [ { "creatorType": "author", "name": "M. Gokul Prasad" }, { "creatorType": "author", "name": "T. Sumathi" }, { "creatorType": "author", "name": "M. Hemalatha" } ], "abstractNote": "Over years the searches made over web still remains non semantic. The internet media is filled with lot of unstructured data that results this state of non semantic search over web. To make the search more specific over the query given, its attributes are to be defined while the search along with its object name. By defining so the normal web search appears to be semantic. The attributes relevant to the context is made easy over text, audio and video files. Whereas Image files still suffers with its undefined attributes along with the relevant image being uploaded. Moreover the images over web aren’t tagged yet with relevant attributes in them. JPEG, Bitmap and all other commonly used image supporters yet remain without tags. Only this leads a non semantic search over images on web.", "publicationTitle": "International Journal of Computer Applications", "publisher": "", "place": "", "date": "2011", "volume": "17", "issue": "8", "section": "", "partNumber": "", "partTitle": "", "pages": "39-42", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "http://utexas.summon.serialssolutions.com/link/0/eLvHCXMwTV27CgIxEAyiYC1oWn8gR16X5OrzguCjOkXtkk1SCqL_j3ungtUyabbYMDMhDEvI2g02W4EwcfjWBB7qUKxLOPykNS9DbrjvrD-bzcHt_tjcL8gk35fk5Lu-3bLvMgAG6NgFEwqFV2sd0M-AkQ4yHljQMaGkcdnIqASgeEGpo-Q2GBhDoYGnrDiIIlZkig_qTMms4GCxItlSbEzJ_NK01-Nt7z9w8YPVcww-VY8XRW4f7wWTFX8DMPY0YA", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0975-8887", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "Journal Article", "tags": [], "collections": [], "relations": {}, "dateAdded": "2013-10-07T15:17:48Z", "dateModified": "2013-10-07T15:17:48Z" } }, { "key": "RXNIVMGI", "version": 2, "library": { "type": "group", "id": 220969, "name": "HCI Project", "links": { "alternate": { "href": "https://www.zotero.org/groups/hci_project", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/220969/items/RXNIVMGI", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/hci_project/items/RXNIVMGI", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1268205, "username": "elovero", "name": "Elizabeth Lovero", "links": { "alternate": { "href": "https://www.zotero.org/elovero", "type": "text/html" } } }, "creatorSummary": "Jiang et al.", "parsedDate": "2013", "numChildren": 2 }, "data": { "key": "RXNIVMGI", "version": 2, "itemType": "journalArticle", "title": "Query-Adaptive Image Search With Hash Codes", "creators": [ { "creatorType": "author", "firstName": "Yu-Gang", "lastName": "Jiang" }, { "creatorType": "author", "firstName": "Jun", "lastName": "Wang" }, { "creatorType": "author", "firstName": "Xiangyang", "lastName": "Xue" }, { "creatorType": "author", "firstName": "Shih-Fu", "lastName": "Chang" } ], "abstractNote": "Scalable image search based on visual similarity has been an active topic of research in recent years. State-of-the-art solutions often use hashing methods to embed high-dimensional image features into Hamming space, where search can be performed in real-time based on Hamming distance of compact hash codes. Unlike traditional metrics (e.g., Euclidean) that offer continuous distances, the Hamming distances are discrete integer values. As a consequence, there are often a large number of images sharing equal Hamming distances to a query, which largely hurts search results where fine-grained ranking is very important. This paper introduces an approach that enables query-adaptive ranking of the returned images with equal Hamming distances to the queries. This is achieved by firstly offline learning bitwise weights of the hash codes for a diverse set of predefined semantic concept classes. We formulate the weight learning process as a quadratic programming problem that minimizes intra-class distance while preserving inter-class relationship captured by original raw image features. Query-adaptive weights are then computed online by evaluating the proximity between a query and the semantic concept classes. With the query-adaptive bitwise weights, returned images can be easily ordered by weighted Hamming distance at a finer-grained hash code level rather than the original Hamming distance level. Experiments on a Flickr image dataset show clear improvements from our proposed approach.", "publicationTitle": "IEEE Transactions on Multimedia", "publisher": "", "place": "", "date": "2013", "volume": "15", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "442-453", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1109/TMM.2012.2231061", "citationKey": "", "url": "http://utexas.summon.serialssolutions.com/link/0/eLvHCXMwTV1NC8IwDC2i4FnQXv0DHe3S9eM8NwQ_TlPUW5t2R0H0_2M2FYRAyCmBFx4J4RHG1m4YswGVicNZE2WoQm9dIvCT1rIfdMNdY9uz2Rzc7o_N2wWb5PuSndqmq7fi-wxAYKmMEwkwBw8VZmUMZhshQnABvURdyexLE6VDl3PQmCyEREbVAYAyqCCqFZvSQp05m_UELHkiW06JOZtffH093vbtJ1z8wuI5Cp-Kx4sTt499IcpCvgE58zaj", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "1520-9210", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "Journal Article", "tags": [ { "tag": "Algorithms" }, { "tag": "Educational institutions" }, { "tag": "Electronic mail" }, { "tag": "Hamming distance" }, { "tag": "Image retrieval" }, { "tag": "Indexing" }, { "tag": "Query-adaptive image search" }, { "tag": "Real-time systems" }, { "tag": "Semantics" }, { "tag": "Studies" }, { "tag": "Visualization" }, { "tag": "hash codes" }, { "tag": "scalability" }, { "tag": "weighted Hamming distance" } ], "collections": [], "relations": {}, "dateAdded": "2013-10-07T15:17:48Z", "dateModified": "2013-10-07T15:17:48Z" } }, { "key": "BQPIK25W", "version": 2, "library": { "type": "group", "id": 220969, "name": "HCI Project", "links": { "alternate": { "href": "https://www.zotero.org/groups/hci_project", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/220969/items/BQPIK25W", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/hci_project/items/BQPIK25W", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1268205, "username": "elovero", "name": "Elizabeth Lovero", "links": { "alternate": { "href": "https://www.zotero.org/elovero", "type": "text/html" } } }, "creatorSummary": "Glassner", "parsedDate": "2003", "numChildren": 2 }, "data": { "key": "BQPIK25W", "version": 2, "itemType": "journalArticle", "title": "Image search and replace", "creators": [ { "creatorType": "author", "firstName": "A.", "lastName": "Glassner" } ], "abstractNote": "Discusses building a system that will let us perform a search and replace on raster images. if we provide a source picture, a target image, and a replacement image, the system can remove every instance of the target from the source and insert in its place a copy of the replacement. of course, implementing this is a little more complicated.", "publicationTitle": "IEEE Computer Graphics and Applications", "publisher": "", "place": "", "date": "2003", "volume": "23", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "80-88", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1109/MCG.2003.1198266", "citationKey": "", "url": "http://utexas.summon.serialssolutions.com/link/0/eLvHCXMwTV1NC8IwDC2i4FnQXv0DHWu2td15rgh-nKaot37lKIj-f0yngqeQUw4JL-8RkjC2NplmV0Eqn8eaoXSNQ20iJT_WdYl5b3jotT2rzcHs_tDcLtgk3ZfsZPuh24rvMwARqANJYSBFQEiVLqPCKmC-e54iCXKfvALpjXKSFJeXtZMhRq2xBcwEpYHQgJMrNiVBnTibISWWLIEtp8CczS9tdz3e9vbjLn5u8RwXn4rHixO2j3UhoCjfttw1XQ", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0272-1716", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "Generic", "tags": [ { "tag": "computer graphics" }, { "tag": "image replace" }, { "tag": "image search" }, { "tag": "image texture" }, { "tag": "raster images" }, { "tag": "replacement image" }, { "tag": "source picture" }, { "tag": "target image" }, { "tag": "texture expansion algorithms" } ], "collections": [], "relations": {}, "dateAdded": "2013-10-07T15:17:48Z", "dateModified": "2013-10-07T15:17:48Z" } }, { "key": "MPXJRQMS", "version": 2, "library": { "type": "group", "id": 220969, "name": "HCI Project", "links": { "alternate": { "href": "https://www.zotero.org/groups/hci_project", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/220969/items/MPXJRQMS", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/hci_project/items/MPXJRQMS", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1268205, "username": "elovero", "name": "Elizabeth Lovero", "links": { "alternate": { "href": "https://www.zotero.org/elovero", "type": "text/html" } } }, "creatorSummary": "Chi-Wing Lo et al.", "parsedDate": "2002", "numChildren": 2 }, "data": { "key": "MPXJRQMS", "version": 2, "itemType": "conferencePaper", "title": "Character-image search engine", "creators": [ { "creatorType": "author", "name": "Chi-Wing Lo" }, { "creatorType": "author", "name": "Qin Lu" }, { "creatorType": "author", "name": "Kwun-Tak Ng" } ], "abstractNote": "In common Internet browsers, a user can search text within a Web page or a frame that the user is currently reading. The search algorithms used to search the source code of the HTML page, and a browser built in search program also highlights the matched words in the browser display. However, when users browse Web sites through some character-to-image conversion gateway, all characters are converted to images by using the same character to image conversion technology. In this case, the browser built-in search utility no longer works because it is impossible to search images in current implementations. We present a character-image search engine that provides character search functionality in a page with embedded character images. The engine works with a character-to-image conversion gateway using an agreed method, thus the character images can be treated just as characters in the search process. The provision of such a character-image search function makes the character-to-image conversion gateway more convenient to use. 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