Abstract: A method for information retrieval includes querying a multimedia collection with a first component of a multimedia query to generate first comparison measures between the first component of the query and respective objects in the collection for a first media type. The collection is queried with a second component of the multimedia query to generate second comparison measures between the second component of the query and respective objects in the collection for a second media type. An aggregated score for each of a set of objects in the collection is computed by applying an aggregating function in which a first confidence weighting is applied to the first comparison measure and a second confidence weighting is applied to the second comparison measure. The first confidence weighting is independent of the second comparison measure. The second confidence weighting is dependent on the first comparison measure.

Abstract: A system and method for information retrieval are disclosed. The method includes querying a multimedia collection with a first component of a multimedia query (e.g., a text-based part of the query) to generate first comparison measures between the first component of the query and respective objects in the collection for a first media type (e.g., text). The multimedia collection is queried with a second component of the multimedia query (e.g., an image-based part of the query) to generate second comparison measures between the second component of the query and respective objects in the collection for a second media type (e.g., visual). An aggregated score for each of a set of objects in the collection is computed, based on the first comparison measure and the second comparison measure for the object.

Abstract: A method of linear unsupervised classification allowing a database composed of objects and of descriptors to be structured, which is stable on the order of the objects, comprises an initial step for transformation of the qualitative, quantitative or textual data into presence-absence binary data. A structural threshold ?s function is determined of the n2 agreements between the objects to be classified with the structural threshold defining an optimization criterion adapted to the data. The descriptors are used as structuring and construction generators of a partition or set of classes. A class generated by a descriptor and a partition (40, 41, 42) progressively merged. For an optimization criterion involving a function ƒ(Cii,Ci?i?)=Min(Cii,Ci?i?), sums of Minimum functions are linearized.

Abstract: A soft clustering method comprises (i) grouping items into non-exclusive cliques based on features associated with the items, and (ii) clustering the non-exclusive cliques using a hard clustering algorithm to generate item groups on the basis of mutual similarity of the features of the items constituting the cliques. In some named entity recognition embodiments illustrated herein as examples, named entities together with contexts are grouped into cliques based on mutual context similarity. Each clique includes a plurality of different named entities having mutual context similarity. The cliques are clustered to generate named entity groups on the basis of mutual similarity of the contexts of the named entities constituting the cliques.

Abstract: A system and method for information retrieval are disclosed. The method includes querying a multimedia collection with a first component of a multimedia query (e.g., a text-based part of the query) to generate first comparison measures between the first component of the query and respective objects in the collection for a first media type (e.g., text). The multimedia collection is queried with a second component of the multimedia query (e.g., an image-based part of the query) to generate second comparison measures between the second component of the query and respective objects in the collection for a second media type (e.g., visual). An aggregated score for each of a set of objects in the collection is computed, based on the first comparison measure and the second comparison measure for the object.

Abstract: An apparatus and method facilitate combined query based searching with serendipitous browsing in a multimedia collection. A user selects objects to label from a local map, which may include representations of objects retrieved from the collection as being responsive to a text or image base query. The text and image portions of the object can be independently labeled. Unlabeled objects are scored and ranked based on the applied labels of labeled objects, which may take into account cross-media pseudo-relevance and user selectable (or default) parameters, such as a forgetting factor, which tends to place greater weight on more recently labeled objects, and a modality parameter, which laces greater weight on the modality (text, image, or hybrid) currently selected by the user. The local map is modified, based on the ranking, optionally after reranking of objects to improve the diversity of the displayed objects.

Abstract: A fusion system fuses M rankings generated by M judges by (i) computing values of an aggregation function for items of the M rankings, the aggregation function including a sum of pairwise conjunctions of ranking values of different judges for an input item, and (ii) constructing an aggregation ranking based on the aggregation function values. In an illustrative application, the judges are different Internet search engines and the rankings are sets of search engine results generated for a query input to the search engines, and a consensus search result corresponding to the query is defined by the aggregation ranking. In another illustrative application, the judges are different soft classifiers, and the rankings are probability vectors generated for an input object by the different soft classifiers, and the input object is classified based on a consensus probability vector defined by the aggregation ranking.

Abstract: An apparatus and method facilitate combined query based searching with serendipitous browsing in a multimedia collection. A user selects objects to label from a local map, which may include representations of objects retrieved from the collection as being responsive to a text or image base query. The text and image portions of the object can be independently labeled. Unlabeled objects are scored and ranked based on the applied labels of labeled objects, which may take into account cross-media pseudo-relevance and user selectable (or default) parameters, such as a forgetting factor, which tends to place greater weight on more recently labeled objects, and a modality parameter, which laces greater weight on the modality (text, image, or hybrid) currently selected by the user. The local map is modified, based on the ranking, optionally after reranking of objects to improve the diversity of the displayed objects.

Abstract: A fusion system fuses M rankings generated by M judges by (i) computing values of an aggregation function for items of the M rankings, the aggregation function including a sum of pairwise conjunctions of ranking values of different judges for an input item, and (ii) constructing an aggregation ranking based on the aggregation function values. In an illustrative application, the judges are different Internet search engines and the rankings are sets of search engine results generated for a query input to the search engines, and a consensus search result corresponding to the query is defined by the aggregation ranking. In another illustrative application, the judges are different soft classifiers, and the rankings are probability vectors generated for an input object by the different soft classifiers, and the input object is classified based on a consensus probability vector defined by the aggregation ranking.

Abstract: A soft clustering method comprises (i) grouping items into non-exclusive cliques based on features associated with the items, and (ii) clustering the non-exclusive cliques using a hard clustering algorithm to generate item groups on the basis of mutual similarity of the features of the items constituting the cliques. In some named entity recognition embodiments illustrated herein as examples, named entities together with contexts are grouped into cliques based on mutual context similarity. Each clique includes a plurality of different named entities having mutual context similarity. The cliques are clustered to generate named entity groups on the basis of mutual similarity of the contexts of the named entities constituting the cliques.

Abstract: A method of linear unsupervised classification allowing a database composed of objects and of descriptors to be structured, which is stable on the order of the objects, comprises an initial step for transformation of the qualitative, quantitative or textual data into presence-absence binary data. A structural threshold ?s function is determined of the n2 agreements between the objects to be classified with the structural threshold defining an optimization criterion adapted to the data. The descriptors are used as structuring and construction generators of a partition or set of classes. A class generated by a descriptor and a partition (40, 41, 42) progressively merged. For an optimization criterion involving a function ƒ(Cii,Ci?i?)=Min(Cii,Ci?i?), sums of Minimum functions are linearized.