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Recognition by directed attention to recursively partitioned images
A learning/recognition model (and instantiating program) is described which recursively combines the learning paradigms of conceptual clustering (Michalski, 1980) and learning-from-examples to resolve the ambiguities of real-world recognition. The model is based on neuropsychological and psychological evidence that the visual system is analytic, hierarchical, and composed of a parallel/serial dichotomy (many, see conclusions by Crick, 1984). Emulating the experimental evidence, parallel processes in the model decompose the image into components and cluster the constituents in much the same way as the image processing technique known as moment analysis (Alt, 1962). Serial, attentive mechanisms then reassemble the decompositions by investigating spatial relationships between components. The use of attentive mechanisms extends the moment analysis technique to handle alterations in structure and solves the contention problem created by combining the two learning paradigms. The contention results from a disagreement between the teacher and the model on what constitutes the salient features at the highest level of the symbol. There are four cases ZBT must handle, two of which result from the disagreement with the teacher. The parallel/serial dichotomy represents a vertical/horizontal tradeoff between the invariant and variant features of a domain. The resultant learned hierarchy allows ZBT to recognize structural differences while avoiding problems of exponential growth
Multi modal multi-semantic image retrieval
PhDThe rapid growth in the volume of visual information, e.g. image, and video can
overwhelm users’ ability to find and access the specific visual information of interest
to them. In recent years, ontology knowledge-based (KB) image information retrieval
techniques have been adopted into in order to attempt to extract knowledge from these
images, enhancing the retrieval performance. A KB framework is presented to
promote semi-automatic annotation and semantic image retrieval using multimodal
cues (visual features and text captions). In addition, a hierarchical structure for the KB
allows metadata to be shared that supports multi-semantics (polysemy) for concepts.
The framework builds up an effective knowledge base pertaining to a domain specific
image collection, e.g. sports, and is able to disambiguate and assign high level
semantics to ‘unannotated’ images.
Local feature analysis of visual content, namely using Scale Invariant Feature
Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’
model (BVW) as an effective method to represent visual content information and to
enhance its classification and retrieval. Local features are more useful than global
features, e.g. colour, shape or texture, as they are invariant to image scale, orientation
and camera angle. An innovative approach is proposed for the representation,
annotation and retrieval of visual content using a hybrid technique based upon the use
of an unstructured visual word and upon a (structured) hierarchical ontology KB
model. The structural model facilitates the disambiguation of unstructured visual
words and a more effective classification of visual content, compared to a vector
space model, through exploiting local conceptual structures and their relationships.
The key contributions of this framework in using local features for image
representation include: first, a method to generate visual words using the semantic
local adaptive clustering (SLAC) algorithm which takes term weight and spatial
locations of keypoints into account. Consequently, the semantic information is
preserved. Second a technique is used to detect the domain specific ‘non-informative
visual words’ which are ineffective at representing the content of visual data and
degrade its categorisation ability. Third, a method to combine an ontology model with
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a visual word model to resolve synonym (visual heterogeneity) and polysemy
problems, is proposed. The experimental results show that this approach can discover
semantically meaningful visual content descriptions and recognise specific events,
e.g., sports events, depicted in images efficiently.
Since discovering the semantics of an image is an extremely challenging problem, one
promising approach to enhance visual content interpretation is to use any associated
textual information that accompanies an image, as a cue to predict the meaning of an
image, by transforming this textual information into a structured annotation for an
image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct
types of information representation and modality, there are some strong, invariant,
implicit, connections between images and any accompanying text information.
Semantic analysis of image captions can be used by image retrieval systems to
retrieve selected images more precisely. To do this, a Natural Language Processing
(NLP) is exploited firstly in order to extract concepts from image captions. Next, an
ontology-based knowledge model is deployed in order to resolve natural language
ambiguities. To deal with the accompanying text information, two methods to extract
knowledge from textual information have been proposed. First, metadata can be
extracted automatically from text captions and restructured with respect to a semantic
model. Second, the use of LSI in relation to a domain-specific ontology-based
knowledge model enables the combined framework to tolerate ambiguities and
variations (incompleteness) of metadata. The use of the ontology-based knowledge
model allows the system to find indirectly relevant concepts in image captions and
thus leverage these to represent the semantics of images at a higher level.
Experimental results show that the proposed framework significantly enhances image
retrieval and leads to narrowing of the semantic gap between lower level machinederived
and higher level human-understandable conceptualisation
An information-driven framework for image mining
[Abstract]: Image mining systems that can automatically extract semantically meaningful information (knowledge) from image data are increasingly in demand. The fundamental challenge in image mining is to determine how low-level, pixel representation contained in a raw image or
image sequence can be processed to identify high-level spatial objects and relationships. To meet
this challenge, we propose an efficient information-driven framework for image mining. We distinguish four levels of information: the Pixel Level, the Object Level, the Semantic Concept Level, and the Pattern and Knowledge Level. High-dimensional indexing schemes and retrieval
techniques are also included in the framework to support the flow of information among the levels. We believe this framework represents the first step towards capturing the different levels of information present in image data and addressing the issues and challenges of discovering useful
patterns/knowledge from each level
A lightweight web video model with content and context descriptions for integration with linked data
The rapid increase of video data on the Web has warranted an urgent need for effective representation, management and retrieval of web videos. Recently, many studies have been carried out for ontological representation of videos, either using domain dependent or generic schemas such as MPEG-7, MPEG-4, and COMM. In spite of their extensive coverage and sound theoretical grounding, they are yet to be widely used by users. Two main possible reasons are the complexities involved and a lack of tool support. We propose a lightweight video content model for content-context description and integration. The uniqueness of the model is that it tries to model the emerging social context to describe and interpret the video. Our approach is grounded on exploiting easily extractable evolving contextual metadata and on the availability of existing data on the Web. This enables representational homogeneity and a firm basis for information integration among semantically-enabled data sources. The model uses many existing schemas to describe various ontology classes and shows the scope of interlinking with the Linked Data cloud
Mind the Gap: Another look at the problem of the semantic gap in image retrieval
This paper attempts to review and characterise the problem of the semantic gap in image retrieval and the attempts being made to bridge it. In particular, we draw from our own experience in user queries, automatic annotation and ontological techniques. The first section of the paper describes a characterisation of the semantic gap as a hierarchy between the raw media and full semantic understanding of the media's content. The second section discusses real users' queries with respect to the semantic gap. The final sections of the paper describe our own experience in attempting to bridge the semantic gap. In particular we discuss our work on auto-annotation and semantic-space models of image retrieval in order to bridge the gap from the bottom up, and the use of ontologies, which capture more semantics than keyword object labels alone, as a technique for bridging the gap from the top down
Language and memory for object location
In three experiments, we investigated the influence of two types of language on memory for object location: demonstratives (this, that) and possessives (my, your). Participants first read instructions containing demonstratives/possessives to place objects at different locations, and then had to recall those object locations (following object removal). Experiments 1 and 2 tested contrasting predictions of two possible accounts of language on object location memory: the Expectation Model (Coventry, Griffiths, & Hamilton, 2014) and the congruence account (Bonfiglioli, Finocchiaro, Gesierich, Rositani, & Vescovi, 2009). In Experiment 3, the role of attention allocation as a possible mechanism was investigated. Results across all three experiments show striking effects of language on object location memory, with the pattern of data supporting the Expectation Model. In this model, the expected location cued by language and the actual location are concatenated leading to (mis)memory for object location, consistent with models of predictive coding (Bar, 2009; Friston, 2003)
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