Large- Scale Content Based Face Image Retrieval using Attribute Enhanced Sparse Codewords.

Content based image retrieval (CBIR) have turn into majority dynamic exploration regions within previous couple of existence. Numerous index strategies be in light of worldwide component circulations. Be that as it may, these worldwide circulations have restricted segregating force since they are not able to catch nearby picture data. Photographs with individuals are the foremost attention of users. Consequently with exponentially increasing pictures, huge size contented base features representation recovery is a facilitating knowledge in favor of various developing applications. The main objective is to apply automatically spotted human characteristics that comprise semantic cue of facade pictures toward increase gratified base facade recovery through creating semantic codeword pro effectual huge size countenance recovery. With leveraging person characteristics into scalable as well as methodical structure, suggest and offer two orthogonal systems named attribute improved meager code and attribute entrenched upturned index toward develop facade recovery. We compare proposed method with other three methods namely LBP, ATTR and SC methods. The results illustrate that the proposed methods can attain qualified enhancement in Mean Average Precision (MAP) associated to the existing methods. DOI: 10.17762/ijritcc2321-8169.15084

A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur

A Short Survey on Data Clustering Algorithms

With rapidly increasing data, clustering algorithms are important tools for data analytics in modern research. They have been successfully applied to a wide range of domains; for instance, bioinformatics, speech recognition, and financial analysis. Formally speaking, given a set of data instances, a clustering algorithm is expected to divide the set of data instances into the subsets which maximize the intra-subset similarity and inter-subset dissimilarity, where a similarity measure is defined beforehand. In this work, the state-of-the-arts clustering algorithms are reviewed from design concept to methodology; Different clustering paradigms are discussed. Advanced clustering algorithms are also discussed. After that, the existing clustering evaluation metrics are reviewed. A summary with future insights is provided at the end

Creative Discovery in Architectural Design Processes: An empirical study of procedural and contextual components

This research aims to collect empirical evidence on the nature of design by investigating the question: What role do procedural activities (where each design step reflects a unit in a linear process) and contextual activities (an action based on the situation, environment and affordances) play in the generation of creative insights, critical moves, and the formation of design concepts in the reasoning process? The thesis shows how these activities can be identified through the structure of a linkograph, for better understanding the conditions under which creativity and innovation take place. Adopting a mixed methodology, a deductive approach evaluates the existing models that aim to capture the series of design events, while an inductive approach collects data and ethnographic observations for an empirical study of architectural design experiments based on structured and unstructured briefs. A joint approach of quantitative and qualitative analyses is developed to detect the role of evolving actions and structural units of reasoning, particularly the occurrence of creative insights (‘eureka’ and ‘aha!’ moments) in the formation of concepts by judging the gradual transformation of mental imagery and external representations in the sketching process. The findings of this research are: (1) For any design process procedural components are subsets in solving the design problem for synchronic concept development or implementation of the predefined conceptual idea, whereas contextual components relate to a comprehensive view to solve the design problem through concept synthesis of back- and forelinking between the diachronic stages of the design process. (2) This study introduces a new method of looking at evolving design moves and critical actions by considering the time of emergence in the structure of the reasoning process. Directed linkography compares two different situations: the first is synchronous, looking at relations back to preceding events, and the second is diachronic, looking at the design state after completion. Accordingly, creative insights can be categorised into those emerging in incremental reasoning to reframe the solution, and sudden mental insights emerging in non-incremental reasoning to restructure the design problem and reformulate the entire design configuration. (3) Two architectural designing styles are identified: some architects define the design concept early, set goals and persevere in framing and reframing this until the end, whereas others initiate the concept by designing independent conceptual elements and then proceed to form syntheses for the design configuration. Sudden mental insights are most likely to emerge from the unexpected combination of synthesis, particularly in the latter style. In its contribution to design research and creative cognition this dissertation paves the way for a better understanding of the role of reflective practices in design creativity and cognitive processes and presents new insights into what it means to think and design as an architect

Learning Behavioural Context

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