33,958 research outputs found
Dynamic Learning of Indexing Concepts for Home Image Retrieval
International audienceThis paper presents a component of a content based image retrieval system dedicated to let a user define the indexing terms used later during retrieval. A user inputs a indexing term name, image examples and counter-examples of the term,and the system learns a model of the concept as well as a similarity measure for this term. The similarity measure is based on weights reflecting the importance of each low-level feature extracted from the images. The system computes these weights using a genetic algorithm. Rating a particular similarity measure is done by clustering the examples and counter-examples using these weights and computing the quality of the obtained clusters. Experiments are conducted and results are presented on a set of 600 images
A framework for automatic semantic video annotation
The rapidly increasing quantity of publicly available videos has driven research into developing automatic tools for indexing, rating, searching and retrieval. Textual semantic representations, such as tagging, labelling and annotation, are often important factors in the process of indexing any video, because of their user-friendly way of representing the semantics appropriate for search and retrieval. Ideally, this annotation should be inspired by the human cognitive way of perceiving and of describing videos. The difference between the low-level visual contents and the corresponding human perception is referred to as the ‘semantic gap’. Tackling this gap is even harder in the case of unconstrained videos, mainly due to the lack of any previous information about the analyzed video on the one hand, and the huge amount of generic knowledge required on the other. This paper introduces a framework for the Automatic Semantic Annotation of unconstrained videos. The proposed framework utilizes two non-domain-specific layers: low-level visual similarity matching, and an annotation analysis that employs commonsense knowledgebases. Commonsense ontology is created by incorporating multiple-structured semantic relationships. Experiments and black-box tests are carried out on standard video databases for action recognition and video information retrieval. White-box tests examine the performance of the individual intermediate layers of the framework, and the evaluation of the results and the statistical analysis show that integrating visual similarity matching with commonsense semantic relationships provides an effective approach to automated video annotation
Structured Knowledge Representation for Image Retrieval
We propose a structured approach to the problem of retrieval of images by
content and present a description logic that has been devised for the semantic
indexing and retrieval of images containing complex objects. As other
approaches do, we start from low-level features extracted with image analysis
to detect and characterize regions in an image. However, in contrast with
feature-based approaches, we provide a syntax to describe segmented regions as
basic objects and complex objects as compositions of basic ones. Then we
introduce a companion extensional semantics for defining reasoning services,
such as retrieval, classification, and subsumption. These services can be used
for both exact and approximate matching, using similarity measures. Using our
logical approach as a formal specification, we implemented a complete
client-server image retrieval system, which allows a user to pose both queries
by sketch and queries by example. A set of experiments has been carried out on
a testbed of images to assess the retrieval capabilities of the system in
comparison with expert users ranking. Results are presented adopting a
well-established measure of quality borrowed from textual information
retrieval
Layout-based substitution tree indexing and retrieval for mathematical expressions
We introduce a new system for layout-based indexing and retrieval of mathematical expressions using substitution trees. Substitution trees can efficiently store and find hierarchically-structured data based on similarity. Previously Kolhase and Sucan applied substitution trees to indexing mathematical expressions in operator tree representation (Content MathML) and query-by-expression retrieval. In this investigation, we use substitution trees to index mathematical expressions in symbol layout tree representation (LaTeX) to group expressions based on the similarity of their symbols, symbol layout, sub-expressions and size. We describe our novel substitution tree indexing and retrieval algorithms and our many significant contributions to the behavior of these algorithms, including: allowing substitution trees to index and retrieve layout-based mathematical expressions instead of predicates; introducing a bias in the insertion function that helps group expressions in the index based on similarity in baseline size; modifying the search function to find expressions that are not identical yet still structurally similar to a search query; and ranking search results based on their similarity in symbols and symbol layout to the search query. We provide an experiment testing our system against the term frequency-inverse document frequency (TF-IDF) keyword-based system of Zanibbi and Yuan and demonstrate that: in many cases, the two systems are comparable; our system excelled at finding expressions identical to the search query and expressions containing relevant sub-expressions; and our system experiences some limitations due to the insertion bias and the presence of LaTeX formatting in expressions. Future work includes: designing a different insertion bias that improves the quality of search results; modifying the behavior of the search and ranking functions; and extending the scope of the system so that it can index websites or non-LaTeX expressions (such as MathML or images). Overall, we present a promising first attempt at layout-based substitution tree indexing and retrieval for mathematical expressions
Local Triangular Kernel-Based Clustering (LTKC) for Case Indexing on Case-Based Reasoning
This study aims to improve the performance of Case-Based Reasoning by utilizing cluster analysis which is used as an indexing method to speed up case retrieval in CBR. The clustering method uses Local Triangular Kernel-based Clustering (LTKC). The cosine coefficient method is used for finding the relevant cluster while similarity value is calculated using Manhattan distance, Euclidean distance, and Minkowski distance. Results of those methods will be compared to find which method gives the best result. This study uses three test data: malnutrition disease, heart disease, and thyroid disease. Test results showed that CBR with LTKC-indexing has better accuracy and processing time than CBR without indexing. The best accuracy on threshold 0.9 of malnutrition disease, obtained using the Euclidean distance which produces 100% accuracy and 0.0722 seconds average retrieval time. The best accuracy on threshold 0.9 of heart disease, obtained using the Minkowski distance which produces 95% accuracy and 0.1785 seconds average retrieval time. The best accuracy on threshold 0.9 of thyroid disease, obtained using the Minkowski distance which produces 92.52% accuracy and 0.3045 average retrieval time. The accuracy comparison of CBR with SOM-indexing, DBSCAN-indexing, and LTKC-indexing for malnutrition diseases and heart disease resulted that they have almost equal accuracy
New Method for 3D Shape Retrieval
The recent technological progress in acquisition, modeling and processing of
3D data leads to the proliferation of a large number of 3D objects databases.
Consequently, the techniques used for content based 3D retrieval has become
necessary. In this paper, we introduce a new method for 3D objects recognition
and retrieval by using a set of binary images CLI (Characteristic level
images). We propose a 3D indexing and search approach based on the similarity
between characteristic level images using Hu moments for it indexing. To
measure the similarity between 3D objects we compute the Hausdorff distance
between a vectors descriptor. The performance of this new approach is evaluated
at set of 3D object of well known database, is NTU (National Taiwan University)
database.Comment: 10 pages, 5 figures, publication pape
Composite Correlation Quantization for Efficient Multimodal Retrieval
Efficient similarity retrieval from large-scale multimodal database is
pervasive in modern search engines and social networks. To support queries
across content modalities, the system should enable cross-modal correlation and
computation-efficient indexing. While hashing methods have shown great
potential in achieving this goal, current attempts generally fail to learn
isomorphic hash codes in a seamless scheme, that is, they embed multiple
modalities in a continuous isomorphic space and separately threshold embeddings
into binary codes, which incurs substantial loss of retrieval accuracy. In this
paper, we approach seamless multimodal hashing by proposing a novel Composite
Correlation Quantization (CCQ) model. Specifically, CCQ jointly finds
correlation-maximal mappings that transform different modalities into
isomorphic latent space, and learns composite quantizers that convert the
isomorphic latent features into compact binary codes. An optimization framework
is devised to preserve both intra-modal similarity and inter-modal correlation
through minimizing both reconstruction and quantization errors, which can be
trained from both paired and partially paired data in linear time. A
comprehensive set of experiments clearly show the superior effectiveness and
efficiency of CCQ against the state of the art hashing methods for both
unimodal and cross-modal retrieval
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