3 research outputs found
Food for talk: photo frames to support social connectedness for elderly people in a nursing home
Social connectedness is crucial to someone’s well-being. A case study is conducted to test whether the social connectedness of elderly people living in a nursing home and their family and friends can be improved through a photo frame. A SIM-based photo frame is used to keep the elderly people informed about the comings and goings of their loved ones. Eight elderly people living in a nursing home participated in this case study for 6-7 weeks. A content analysis of the photos revealed that the photos often were related to special events or holidays that happened in the past. Interviews indicated that the photos mainly served as food for talk, i.e. the photos initiated conversations between the elderly people mutually, with their family members and with the healthcare professionals. They all liked the photo frame and it didn’t serve as a means to exchange news, but as a catalyst to talk –mainly- about the past
Bridging semantic gap: learning and integrating semantics for content-based retrieval
Digital cameras have entered ordinary homes and produced^incredibly large number
of photos. As a typical example of broad image domain, unconstrained consumer
photos vary significantly. Unlike professional or domain-specific images, the objects
in the photos are ill-posed, occluded, and cluttered with poor lighting, focus, and
exposure. Content-based image retrieval research has yet to bridge the semantic gap
between computable low-level information and high-level user interpretation.
In this thesis, we address the issue of semantic gap with a structured learning
framework to allow modular extraction of visual semantics. Semantic image regions
(e.g. face, building, sky etc) are learned statistically, detected directly from image
without segmentation, reconciled across multiple scales, and aggregated spatially to
form compact semantic index. To circumvent the ambiguity and subjectivity in a
query, a new query method that allows spatial arrangement of visual semantics is
proposed. A query is represented as a disjunctive normal form of visual query terms
and processed using fuzzy set operators.
A drawback of supervised learning is the manual labeling of regions as training
samples. In this thesis, a new learning framework to discover local semantic patterns
and to generate their samples for training with minimal human intervention has been
developed. The discovered patterns can be visualized and used in semantic indexing.
In addition, three new class-based indexing schemes are explored. The winnertake-
all scheme supports class-based image retrieval. The class relative scheme and
the local classification scheme compute inter-class memberships and local class patterns
as indexes for similarity matching respectively. A Bayesian formulation is
proposed to unify local and global indexes in image comparison and ranking that
resulted in superior image retrieval performance over those of single indexes.
Query-by-example experiments on 2400 consumer photos with 16 semantic queries
show that the proposed approaches have significantly better (18% to 55%) average
precisions than a high-dimension feature fusion approach. The thesis has paved
two promising research directions, namely the semantics design approach and the
semantics discovery approach. They form elegant dual frameworks that exploits
pattern classifiers in learning and integrating local and global image semantics
Using Dual Cascading Learning Frameworks for Image Indexing
To bridge the semantic gap in content-based image retrieval, detecting meaningful visual entities (e.g. faces, sky, foliage, buildings etc) in image content and classifying images into semantic categories based on trained pattern classifiers have become active research trends. In this paper, we present dual cascading learning frameworks that extract and combine intra-image and inter-class semantics for image indexing and retrieval. In the supervised learning version, support vector detectors are trained on semantic support regions without image segmentation. The reconciled and aggregated detection-based indexes then serve as input for support vector learning of image classifiers to generate class-relative image indexes. During retrieval, similarities based on both indexes are combined to rank images. In the unsupervised learning..