1,477 research outputs found
Aggregated Deep Local Features for Remote Sensing Image Retrieval
Remote Sensing Image Retrieval remains a challenging topic due to the special
nature of Remote Sensing Imagery. Such images contain various different
semantic objects, which clearly complicates the retrieval task. In this paper,
we present an image retrieval pipeline that uses attentive, local convolutional
features and aggregates them using the Vector of Locally Aggregated Descriptors
(VLAD) to produce a global descriptor. We study various system parameters such
as the multiplicative and additive attention mechanisms and descriptor
dimensionality. We propose a query expansion method that requires no external
inputs. Experiments demonstrate that even without training, the local
convolutional features and global representation outperform other systems.
After system tuning, we can achieve state-of-the-art or competitive results.
Furthermore, we observe that our query expansion method increases overall
system performance by about 3%, using only the top-three retrieved images.
Finally, we show how dimensionality reduction produces compact descriptors with
increased retrieval performance and fast retrieval computation times, e.g. 50%
faster than the current systems.Comment: Published in Remote Sensing. The first two authors have equal
contributio
Knowledge Rich Natural Language Queries over Structured Biological Databases
Increasingly, keyword, natural language and NoSQL queries are being used for
information retrieval from traditional as well as non-traditional databases
such as web, document, image, GIS, legal, and health databases. While their
popularity are undeniable for obvious reasons, their engineering is far from
simple. In most part, semantics and intent preserving mapping of a well
understood natural language query expressed over a structured database schema
to a structured query language is still a difficult task, and research to tame
the complexity is intense. In this paper, we propose a multi-level
knowledge-based middleware to facilitate such mappings that separate the
conceptual level from the physical level. We augment these multi-level
abstractions with a concept reasoner and a query strategy engine to dynamically
link arbitrary natural language querying to well defined structured queries. We
demonstrate the feasibility of our approach by presenting a Datalog based
prototype system, called BioSmart, that can compute responses to arbitrary
natural language queries over arbitrary databases once a syntactic
classification of the natural language query is made
Arabic Information Retrieval: A Relevancy Assessment Survey
The paper presents a research in Arabic Information Retrieval (IR). It surveys the impact of statistical and morphological analysis of Arabic text in improving Arabic IR relevancy. We investigated the contributions of Stemming, Indexing, Query Expansion, Text Summarization (TS), Text Translation, and Named Entity Recognition (NER) in enhancing the relevancy of Arabic IR. Our survey emphasizing on the quantitative relevancy measurements provided in the surveyed publications. The paper shows that the researchers achieved significant enhancements especially in building accurate stemmers, with accuracy reaches 97%, and in measuring the impact of different indexing strategies. Query expansion and Text Translation showed positive relevancy effect. However, other tasks such as NER and TS still need more research to realize their impact on Arabic IR
Sub-word indexing and blind relevance feedback for English, Bengali, Hindi, and Marathi IR
The Forum for Information Retrieval Evaluation (FIRE) provides document collections, topics, and relevance assessments for information retrieval (IR) experiments on Indian languages. Several research questions are explored in this paper: 1. how to create create a simple, languageindependent corpus-based stemmer, 2. how to identify sub-words and which types of sub-words are suitable as indexing units, and 3. how to apply blind relevance feedback on sub-words and how feedback term selection is affected by the type of the indexing unit. More than 140 IR experiments are conducted using the BM25 retrieval model on the topic titles and descriptions (TD) for the FIRE 2008 English, Bengali, Hindi, and Marathi document collections. The major findings are: The corpus-based stemming approach is effective as a knowledge-light
term conation step and useful in case of few language-specific resources. For English, the corpusbased
stemmer performs nearly as well as the Porter stemmer and significantly better than the baseline of indexing words when combined with query expansion. In combination with blind relevance feedback, it also performs significantly better than the baseline for Bengali and Marathi IR.
Sub-words such as consonant-vowel sequences and word prefixes can yield similar or better performance in comparison to word indexing. There is no best performing method for all languages. For English, indexing using the Porter stemmer performs best, for Bengali and Marathi, overlapping 3-grams obtain the best result, and for Hindi, 4-prefixes yield the highest MAP. However, in combination with blind relevance feedback using 10 documents and 20 terms, 6-prefixes for English and 4-prefixes for Bengali, Hindi, and Marathi IR yield the highest MAP. Sub-word identification is a general case of decompounding. It results in one or more index terms for a single word form and increases the number of index terms but decreases their average length. The corresponding retrieval experiments show that relevance feedback on sub-words benefits from
selecting a larger number of index terms in comparison with retrieval on word forms. Similarly, selecting the number of relevance feedback terms depending on the ratio of word vocabulary size to sub-word vocabulary size almost always slightly increases information retrieval effectiveness
compared to using a fixed number of terms for different languages
Semantically Enhanced Term Frequency
In this paper, we complement the term frequency, which is used in many bag-of-words based information retrieval models, with information about the semantic relatedness of query and document terms. Our experiments show that when employed in the standard probabilistic retrieval model BM25, the additional semantic information significantly outperforms the standard term frequency, and also improves the effectiveness when additional query expansion is applied. We further analyze the impact of different lexical semantic resources on the IR effectiveness
QueryProp: Object Query Propagation for High-Performance Video Object Detection
Video object detection has been an important yet challenging topic in
computer vision. Traditional methods mainly focus on designing the image-level
or box-level feature propagation strategies to exploit temporal information.
This paper argues that with a more effective and efficient feature propagation
framework, video object detectors can gain improvement in terms of both
accuracy and speed. For this purpose, this paper studies object-level feature
propagation, and proposes an object query propagation (QueryProp) framework for
high-performance video object detection. The proposed QueryProp contains two
propagation strategies: 1) query propagation is performed from sparse key
frames to dense non-key frames to reduce the redundant computation on non-key
frames; 2) query propagation is performed from previous key frames to the
current key frame to improve feature representation by temporal context
modeling. To further facilitate query propagation, an adaptive propagation gate
is designed to achieve flexible key frame selection. We conduct extensive
experiments on the ImageNet VID dataset. QueryProp achieves comparable accuracy
with state-of-the-art methods and strikes a decent accuracy/speed trade-off.
Code is available at https://github.com/hf1995/QueryProp.Comment: This paper is accepted to AAAI202
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Query exhaustivity, relevance feedback and search success in automatic and interactive query expansion
This study explored how the expression of search facets and relevance feedback by users was related to search success in interactive and automatic query expansion in the course of the search process. Search success was measured both in the number of relevant documents retrieved and relevance scores of these items based on a four point scaling. Research design consisted of 26 users searching for four TREC topics in Okapi IR system, half using interactive and half automatic query expansion based on RF. The search logs were recorded, and the users filled in a questionnaire for each topic concerning various features of searching. The results showed that the exhaustivity of the query was the most significant predictor of search success, and that interactive expansion led to better search success than automatic one
Xu: An Automated Query Expansion and Optimization Tool
The exponential growth of information on the Internet is a big challenge for
information retrieval systems towards generating relevant results. Novel
approaches are required to reformat or expand user queries to generate a
satisfactory response and increase recall and precision. Query expansion (QE)
is a technique to broaden users' queries by introducing additional tokens or
phrases based on some semantic similarity metrics. The tradeoff is the added
computational complexity to find semantically similar words and a possible
increase in noise in information retrieval. Despite several research efforts on
this topic, QE has not yet been explored enough and more work is needed on
similarity matching and composition of query terms with an objective to
retrieve a small set of most appropriate responses. QE should be scalable,
fast, and robust in handling complex queries with a good response time and
noise ceiling. In this paper, we propose Xu, an automated QE technique, using
high dimensional clustering of word vectors and Datamuse API, an open source
query engine to find semantically similar words. We implemented Xu as a command
line tool and evaluated its performances using datasets containing news
articles and human-generated QEs. The evaluation results show that Xu was
better than Datamuse by achieving about 88% accuracy with reference to the
human-generated QE.Comment: Accepted to IEEE COMPSAC 201
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Transient expression in Nicotiana benthamiana for rapid functional analysis of genes involved in non-photochemical quenching and carotenoid biosynthesis.
Plants must switch rapidly between light harvesting and photoprotection in response to environmental fluctuations in light intensity. This switch can lead to losses in absorbed energy usage, as photoprotective energy dissipation mechanisms can take minutes to hours to fully relax. One possible way to improve photosynthesis is to engineer these energy dissipation mechanisms (measured as non-photochemical quenching of chlorophyll a fluorescence, NPQ) to induce and relax more quickly, resulting in smaller losses under dynamic light conditions. Previous studies aimed at understanding the enzymes involved in the regulation of NPQ have relied primarily on labor-intensive and time-consuming generation of stable transgenic lines and mutant populations - approaches limited to organisms amenable to genetic manipulation and mapping. To enable rapid functional testing of NPQ-related genes from diverse organisms, we performed Agrobacterium tumefaciens-mediated transient expression assays in Nicotiana benthamiana to test if NPQ kinetics could be modified in fully expanded leaves. By expressing Arabidopsis thaliana genes known to be involved in NPQ, we confirmed the viability of this method for studying dynamic photosynthetic processes. Subsequently, we used naturally occurring variation in photosystem II subunit S, a modulator of NPQ in plants, to explore how differences in amino acid sequence affect NPQ capacity and kinetics. Finally, we functionally characterized four predicted carotenoid biosynthesis genes from the marine algae Nannochloropsis oceanica and Thalassiosira pseudonana and examined the effect of their expression on NPQ in N. benthamiana. This method offers a powerful alternative to traditional gene characterization methods by providing a fast and easy platform for assessing gene function in planta
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