Cell phones and other portable devices are equipped with a variety of technologies by which existing functionality can be improved, and new functionality can be provided. Some relate to visual search capabilities, and determining appropriate actions responsive to different image inputs. Others relate to processing of image data. Still others concern metadata generation, processing, and representation. Yet others relate to coping with fixed focus limitations of cell phone cameras, e.g., in reading digital watermark data. Still others concern user interface improvements. A great number of other features and arrangements are also detailed.

Methods and Systems for Content Processing

An improved human user computer interface system, wherein a user characteristic or set of characteristics, such as demographic profile or societal “role”, is employed to define a scope or domain of operation. The operation itself may be a database search, to interactively define a taxonomic context for the operation, a business negotiation, or other activity. After retrieval of results, a scoring or ranking may be applied according to user define criteria, which are, for example, commensurate with the relevance to the context, but may be, for example, by date, source, or other secondary criteria. A user profile is preferably stored in a computer accessible form, and may be used to provide a history of use, persistent customization, collaborative filtering and demographic information for the user. Advantageously, user privacy and anonymity is maintained by physical and algorithmic controls over access to the personal profiles, and releasing only aggregate data without personally identifying information or of small groups.

Database access system

Automatically describing video content with natural language is a fundamental challenge of computer vision. Re-current Neural Networks (RNNs), which models sequence dynamics, has attracted increasing attention on visual interpretation. However, most existing approaches generate a word locally with the given previous words and the visual content, while the relationship between sentence semantics and visual content is not holistically exploited. As a result, the generated sentences may be contextually correct but the semantics (e.g., subjects, verbs or objects) are not true. This paper presents a novel unified framework, named Long Short-Term Memory with visual-semantic Embedding (LSTM-E), which can simultaneously explore the learning of LSTM and visual-semantic embedding. The former aims to locally maximize the probability of generating the next word given previous words and visual content, while the latter is to create a visual-semantic embedding space for enforcing the relationship between the semantics of the entire sentence and visual content. The experiments on YouTube2Text dataset show that our proposed LSTM-E achieves to-date the best published performance in generating natural sentences: 45.3% and 31.0% in terms of BLEU@4 and METEOR, respectively. Superior performances are also reported on two movie description datasets (M-VAD and MPII-MD). In addition, we demonstrate that LSTM-E outperforms several state-of-the-art techniques in predicting Subject-Verb-Object (SVO) triplets.

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Jointly Modeling Embedding and Translation to Bridge Video and Language

A sequence of cards is stored that graphically represents available options within an information system. In response to a single user action, the sequence of cards is successively displayed within a focus area of the user interface. In response to a subsequent user action, the successive display is discontinued to display a particular card representing a selected option.

System and method for focused navigation within a user interface

Media metadata is accessible for a plurality of media items (See FIG. 12). The media metadata includes a number of strings to identify information regarding the media items (See FIG. 12). Phonetic metadata is associated the number of strings of the media metadata (See FIG. 12). Each portion of the phonetic metadata is stored in an original language of the string (See FIG. 12).

Method and apparatus to control operation of a playback device

Systems and methods for describing video content establish video description records which include an object set (24), an object hierarchy (26) and entity relation graphs (28). Video objects can include global ojects, segment objects and local objects. The video objects are further defined by a number of features organized in classes, which in turn are further defined by a number of feature descriptors (36, 38 and 40). The relationships (44) between and among the objects in the object set (24) are defined by the object hierarchy (26) and entity relation graphs (28). The video description records provide a standard vehicle for describing the content and context of video information for subsequent access and processing by computer applications such as search engines, filters, and archive systems.

Video description system and method

A multimedia archive description scheme is provided for characterizing a multimedia archive having records and associated record descriptions. The multimedia archive description scheme provides a data structure which relates records by similarity measures. The principle data structure in the multimedia archive description scheme is a cluster (100). A cluster includes one or more attributes of the records in the archive and can include one or more cluster relationships (110). Cluster attributes (105) can include feature space attributes, semantic attributes, media attributes and meta attributes of the records in the archive. The cluster relationships (110) can relate records to clusters or clusters to clusters. Cluster relationships can include feature space (syntactic) relationships, sematic relationships, media relationships and meta relationships. The multimedia archive description scheme provides an efficient form for describing a collection of records.

Multimedia archive description scheme

Systems and methods for describing image content establish image description records which include an object set (24), an object hierarchy (26) and entity relation graphs (28). For image content, image objects can include global objects (O0 8) and local objects (O1 2 and O2 6). The image objects are further defined by a number of features of different classes (36, 38 and 40), which in turn are further defined by a number of feature descriptors. The relationships between and among the objects in the object set are defined by the object hierarchy (26) and entity relation graphs (28). The image description records provide a standard vehicle for describing the content and context of image information for subsequent access and processing by computer applications such as search engines, filters, and archive systems.

Chung-Sheng Li

Papers

Video description system and method

Multimedia archive description scheme

Image description system and method