56,704 research outputs found
Query generation from multiple media examples
This paper exploits an unified media document representation called feature terms for query generation from multiple media examples, e.g. images. A feature term refers to a value interval of a media feature. A media document is therefore represented by a frequency vector about feature term appearance. This approach (1) facilitates feature accumulation from multiple examples; (2) enables the exploration of text-based retrieval models for multimedia retrieval. Three statistical criteria, minimised chi-squared, minimised AC/DC rate and maximised entropy, are proposed to extract feature terms from a given media document collection. Two textual ranking functions, KL divergence and a BM25-like retrieval model, are adapted to estimate media document relevance. Experiments on the Corel photo collection and the TRECVid 2006 collection show the effectiveness of feature term based query in image and video retrieval
The study of probability model for compound similarity searching
Information Retrieval or IR system main task is to retrieve relevant documents according to the users query. One of IR most popular retrieval model is the Vector Space Model. This model assumes relevance based on similarity, which is defined as the distance between query and document in the concept space. All currently existing chemical compound database systems have adapt the vector space model to calculate the similarity of a database entry to a query compound. However, it assumes that fragments represented by the bits are independent of one another, which is not necessarily true. Hence, the possibility of applying another IR model is explored, which is the Probabilistic Model, for chemical compound searching. This model estimates the probabilities of a chemical structure to have the same bioactivity as a target compound. It is envisioned that by ranking chemical structures in decreasing order of their probability of relevance to the query structure, the effectiveness of a molecular similarity searching system can be increased. Both fragment dependencies and independencies assumption are taken into consideration in achieving improvement towards compound similarity searching system. After conducting a series of simulated similarity searching, it is concluded that PM approaches really did perform better than the existing similarity searching. It gave better result in all evaluation criteria to confirm this statement. In terms of which probability model performs better, the BD model shown improvement over the BIR model
Query weighting for ranking model adaptation
We propose to directly measure the importance of queries in the source domain to the target domain where no rank labels of documents are available, which is referred to as query weighting. Query weighting is a key step in ranking model adaptation. As the learning object of ranking algorithms is divided by query instances, we argue that it’s more reasonable to conduct importance weighting at query level than document level. We present two query weighting schemes. The first compresses the query into a query feature vector, which aggregates all document instances in the same query, and then conducts query weighting based on the query feature vector. This method can efficiently estimate query importance by compressing query data, but the potential risk is information loss resulted from the compression. The second measures the similarity between the source query and each target query, and then combines these fine-grained similarity values for its importance estimation. Adaptation experiments on LETOR3.0 data set demonstrate that query weighting significantly outperforms document instance weighting methods.
Query by String word spotting based on character bi-gram indexing
In this paper we propose a segmentation-free query by string word spotting
method. Both the documents and query strings are encoded using a recently
proposed word representa- tion that projects images and strings into a common
atribute space based on a pyramidal histogram of characters(PHOC). These
attribute models are learned using linear SVMs over the Fisher Vector
representation of the images along with the PHOC labels of the corresponding
strings. In order to search through the whole page, document regions are
indexed per character bi- gram using a similar attribute representation. On top
of that, we propose an integral image representation of the document using a
simplified version of the attribute model for efficient computation. Finally we
introduce a re-ranking step in order to boost retrieval performance. We show
state-of-the-art results for segmentation-free query by string word spotting in
single-writer and multi-writer standard datasetsComment: To be published in ICDAR201
Neural Vector Spaces for Unsupervised Information Retrieval
We propose the Neural Vector Space Model (NVSM), a method that learns
representations of documents in an unsupervised manner for news article
retrieval. In the NVSM paradigm, we learn low-dimensional representations of
words and documents from scratch using gradient descent and rank documents
according to their similarity with query representations that are composed from
word representations. We show that NVSM performs better at document ranking
than existing latent semantic vector space methods. The addition of NVSM to a
mixture of lexical language models and a state-of-the-art baseline vector space
model yields a statistically significant increase in retrieval effectiveness.
Consequently, NVSM adds a complementary relevance signal. Next to semantic
matching, we find that NVSM performs well in cases where lexical matching is
needed.
NVSM learns a notion of term specificity directly from the document
collection without feature engineering. We also show that NVSM learns
regularities related to Luhn significance. Finally, we give advice on how to
deploy NVSM in situations where model selection (e.g., cross-validation) is
infeasible. We find that an unsupervised ensemble of multiple models trained
with different hyperparameter values performs better than a single
cross-validated model. Therefore, NVSM can safely be used for ranking documents
without supervised relevance judgments.Comment: TOIS 201
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