Availability Of Semantic Information Research Articles

A COMPARISON STUDY ON DEEP LEARNING MODELS FOR BUILDING ROOFTOP CLASSIFICATION

Abstract. The availability of semantic information about a cityscape is essential for understanding and analysing urban processes. Automatic gathering of such information is important due to the enormous amount of data. A great number of building features could be gained solely by visual inspections. Therefore, it is meaningful to utilize recent advancements in automatic image recognition technologies to extract these properties automatically.This paper proposes an optimized solution for the classification of rooftops from aerial imagery based on a deep learning model using Convolutional Neural Networks (CNNs). It describes the architecture of the network, the training procedure and important hypermeters. A model analysis using advanced interpretability and explainability tools is conducted. The model’s superiority is demonstrated by comparing its performance against several state-of-the-art image classification architectures, including CNN-based ones such as Xception and Efficientnet, pure Visual Transformers (ViTs) based architectures such as BEiT, and hybrid architectures.

Annotation propagation in image databases using similarity graphs

The practicality of large-scale image indexing and querying methods depends crucially upon the availability of semantic information. The manual tagging of images with semantic information is in general very labor intensive, and existing methods for automated image annotation may not always yield accurate results. The aim of this paper is to reduce to a minimum the amount of human intervention required in the semantic annotation of images, while preserving a high degree of accuracy. Ideally, only one copy of each object of interest would be labeled manually, and the labels would then be propagated automatically to all other occurrences of the objects in the database. To this end, we propose an influence propagation strategy, SW-KProp , that requires no human intervention beyond the initial labeling of a subset of the images. SW-KProp distributes semantic information within a similarity graph defined on all images in the database: each image iteratively transmits its current label information to its neighbors, and then readjusts its own label according to the combined influences of its neighbors. SW-KProp influence propagation can be efficiently performed by means of matrix computations, provided that pairwise similarities of images are available. We also propose a variant of SW-KProp which enhances the quality of the similarity graph by selecting a reduced feature set for each prelabeled image and rebuilding its neighborhood. The performances of the SW-KProp method and its variant were evaluated against several competing methods on classification tasks for three image datasets: a handwritten digit dataset, a face dataset and a web image dataset. For the digit images, SW-KProp and its variant performed consistently better than the other methods tested. For the face and web images, SW-KProp outperformed its competitors for the case when the number of prelabeled images was relatively small. The performance was seen to improve significantly when the feature selection strategy was applied.

Semantics-based inference algorithms for adaptive visual environments

The paper presents a grammatical inference methodology for the generation of visual languages, that benefits from the availability of semantic information about the sample sentences. Several well-known syntactic inference algorithms are shown to obey a general inference scheme, which the authors call the Gen-Inf scheme. Then, all the algorithms of the Gen-Inf scheme are modified in agreement with the introduced semantics-based inference methodology. The use of grammatical inference techniques in the design of adaptive user interfaces was previously experimented with the VLG system for visual language generation. The system is a powerful tool for specifying, designing, and interpreting customized visual languages for different applications. They enhance the adaptivity of the VLG system to any visual environment by exploiting the proposed semantics-based inference methodology. As a matter of fact, a more general model of visual language generation is achieved, based on the Gen-Inf scheme, where the end-user is allowed to choose the algorithm which best fits his/her requirements within the particular application environment.

Semantic associations and elaborative inference.

In this article, a theoretical framework is proposed for the inference processes that occur during reading. According to the framework, inferences can vary in the degree to which they are encoded. This notion is supported by three experiments in this article that show that degree of encoding can depend on the amount of semantic-associative information available to support the inference processes. In the experiments, test words that express possible inferences from texts are presented for recognition. When testing is delayed, with other texts and test items intervening between a text and its test word, performance depends on the amount of semantic-associative information in the text. If the inferences represented by the test words are not supported by semantic associates in the text, they appear to be only minimally encoded (replicating McKoon & Ratcliff, 1986), but if they are supported by semantic associates, they are strongly encoded. With immediate testing, only 250 ms after the text, performance is shown to depend on semantic-associative information, not on textual information. This suggests that it is the fast availability of semantic information that allows it to support inference processes.

Availability and accessibility of information in the semantic memory of retarded and nonretarded adolescents

The availability of semantic information in storage and the accessibility of that information for retrieval were studied in retarded and nonretarded adolescents. In Experiment 1, 40 normal and 40 CA-equivalent (mean, 15 years) retarded subjects were required to retrieve information from semantic categories as well as to judge whether specific items were members of a given category. The results showed large IQ group differences on the retrieval task which could not be wholly accounted for by the items available in storage. Experiment 2 was designed as an attempt to remediate this retrieval deficit by introducing an organized retrieval plan consisting of subcategory cues. This organization facilitated retrieval as long as it was experimentally provided. Retrieval returned to its original level, however, when the cues were withdrawn. It was concluded that retarded persons had an accessibility deficit in addition to an availability deficit; one aspect of this accessibility deficit involved the failure to use spontaneously mnemonic strategies that were consistent with the semantic organization.

Dual vs. single encoding in recognition memory as a function of lag, delay, and availability of semantic information

One objective of the study was to assess the view of Winograd and Raines (1972) that the absence of a difference between dual as opposed to single encodings of a word at long lags may be due to the fact that subjects can establish two independent temporal tags when a to-be-remembered (TBRI word is presented at long lags. Winograd and Raines' argument that the effectiveness of any such temporal tags should diminish over time was examined. A second objective was to determine whether providing information about the semantic senses used during encoding operations would facilitate recognition, as "sense tagging" theories require. Words were presented twice in either the same low-frequency context or the same high-frequency context, or once in both a high- and low-frequency context at Lags 0 and 15. A recognition test was given immediately, or after a 1-week delay. The test was either context free or each word was accompanied by phrases which, in the case of TBR words, cued subjects with respect to the semantic senses of the word used in initial encoding. All manipulations influenced performance, but not in accordance with theoretical expectations.

The abstraction of linguistic ideas in “meaningless” sentences

It was proposed that the Bransford and Franks linear effect is unrelated to semantic processes and will, therefore, occur even when "meaningless" sentences (i.e., sentences containing nonsense instead of meaningful content words) are employed. Within the Bransford and Franks format, Ss were given either the meaningless sentences or control sentences. Results showed a significant linear effect for the meaningless sentences. Furthermore, although the slope of the effect for meaningless sentences was flatter than that of the control, other data ruled out a semantic integration explanation based on the availability of semantic information contained in sentence structure. A simple guessing strategy hypothesis was offered to account for the linear effect.