285,071 research outputs found
Evaluation of Quranic text retrieval system based on manually indexed topics
This paper investigates the effectiveness of a state of the art information retrieval (IR) system in the verse retrieval problem for Quranic text. The evaluation is based on manually indexed topics of the Quran that provides both the queries and the relevance judgments. Furthermore, the system is evaluated in both Malay and English environment. The performance of the system is measured based on the MAP, the precision at 1, 5 and 10, and the MRR scores. The results of the evaluation are promising, showing the IR system has many potential for the Quranic text retrieval
Episodic memory retrieval, parietal cortex, and the default mode network: Functional and topographic analyses
The default mode network (DMN) is often considered a functionally homogeneous system that is broadly associated with internally directed cognition (e.g., episodic memory, theory of mind, self-evaluation). However, few studies have examined how this network interacts with other networks during putative default processes such as episodic memory retrieval. Using functional magnetic resonance imaging, we investigated the topography and response profile of human parietal regions inside and outside the DMN, independently defined using task-evoked deactivations and resting-state functional connectivity, during episodic memory retrieval. Memory retrieval activated posterior nodes of the DMN, particularly the angular gyrus, but also more anterior and dorsal parietal regions that were anatomically separate from the DMN. The two sets of parietal regions showed different resting-state functional connectivity and response profiles. During memory retrieval, responses in DMN regions peaked sooner than non-DMN regions, which in turn showed responses that were sustained until a final memory judgment was reached. Moreover, a parahippocampal region that showed strong resting-state connectivity with parietal DMN regions also exhibited a pattern of task-evoked activity similar to that exhibited by DMN regions. These results suggest that DMN parietal regions directly supported memory retrieval, whereas non-DMN parietal regions were more involved in postretrieval processes such as memory-based decision making. Finally, a robust functional dissociation within the DMN was observed. Whereas angular gyrus and posterior cingulate/precuneus were significantly activated during memory retrieval, an anterior DMN node in medial prefrontal cortex was strongly deactivated. This latter finding demonstrates functional heterogeneity rather than homogeneity within the DMN during episodic memory retrieval
Efficient On-the-fly Category Retrieval using ConvNets and GPUs
We investigate the gains in precision and speed, that can be obtained by
using Convolutional Networks (ConvNets) for on-the-fly retrieval - where
classifiers are learnt at run time for a textual query from downloaded images,
and used to rank large image or video datasets.
We make three contributions: (i) we present an evaluation of state-of-the-art
image representations for object category retrieval over standard benchmark
datasets containing 1M+ images; (ii) we show that ConvNets can be used to
obtain features which are incredibly performant, and yet much lower dimensional
than previous state-of-the-art image representations, and that their
dimensionality can be reduced further without loss in performance by
compression using product quantization or binarization. Consequently, features
with the state-of-the-art performance on large-scale datasets of millions of
images can fit in the memory of even a commodity GPU card; (iii) we show that
an SVM classifier can be learnt within a ConvNet framework on a GPU in parallel
with downloading the new training images, allowing for a continuous refinement
of the model as more images become available, and simultaneous training and
ranking. The outcome is an on-the-fly system that significantly outperforms its
predecessors in terms of: precision of retrieval, memory requirements, and
speed, facilitating accurate on-the-fly learning and ranking in under a second
on a single GPU.Comment: Published in proceedings of ACCV 201
Unsupervised Graph-based Rank Aggregation for Improved Retrieval
This paper presents a robust and comprehensive graph-based rank aggregation
approach, used to combine results of isolated ranker models in retrieval tasks.
The method follows an unsupervised scheme, which is independent of how the
isolated ranks are formulated. Our approach is able to combine arbitrary
models, defined in terms of different ranking criteria, such as those based on
textual, image or hybrid content representations.
We reformulate the ad-hoc retrieval problem as a document retrieval based on
fusion graphs, which we propose as a new unified representation model capable
of merging multiple ranks and expressing inter-relationships of retrieval
results automatically. By doing so, we claim that the retrieval system can
benefit from learning the manifold structure of datasets, thus leading to more
effective results. Another contribution is that our graph-based aggregation
formulation, unlike existing approaches, allows for encapsulating contextual
information encoded from multiple ranks, which can be directly used for
ranking, without further computations and post-processing steps over the
graphs. Based on the graphs, a novel similarity retrieval score is formulated
using an efficient computation of minimum common subgraphs. Finally, another
benefit over existing approaches is the absence of hyperparameters.
A comprehensive experimental evaluation was conducted considering diverse
well-known public datasets, composed of textual, image, and multimodal
documents. Performed experiments demonstrate that our method reaches top
performance, yielding better effectiveness scores than state-of-the-art
baseline methods and promoting large gains over the rankers being fused, thus
demonstrating the successful capability of the proposal in representing queries
based on a unified graph-based model of rank fusions
Mapping of the wildland fuel characteristics of the Santa Monica mountains of Southern California
LANDSAT digital data was successfully used to map and evaluate the wildland fuels of the Santa Monica Mountains in Southern California. A mixed classification scheme was used where training areas of known vegetation types were entered and the maximum likelihood classifier run, followed by an evaluation of the results and an unsupervised retraining of the classifier using an image of the probability of misclassification. Estimation of maturity class and crown closure percents of the major cover types were assigned to each computer class by associating the photointerpretation of 159 large scale photo samples with the resultant computer classes using analysis of variance and analysis of categorized data. The result of the computer classification and statistical analysis were then transformed from the LANDSAT Coordinate California State Plane Coordinate system for use in a digital format in the FIRESCOPE data retrieval and fire modeling system
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