An Effective Technique for Removal of Facial Dupilcation by SBFA

Search based face annotation (SBFA) is an effective technique to annotate the weakly labeled facial images that are freely available on World Wide Web. The main objective of search based face annotation is to assign correct name labels to given query facial image. One difficult drawback for search based face annotation theme is how to effectively perform annotation by exploiting the list of most similar facial pictures and their weak labels that square measure typically droning and incomplete. To tackle this drawback, we tend to propose a good unattended label refinement (URL) approach for purification the labels of web facial pictures exploitation machine learning technique. We tend to formulate the educational drawback as a gibbose improvement and develop effective improvement algorithms to resolve the large scale learning task expeditiously. To additional speed up the projected theme, we also proposed clustering based approximation algorithmic program which may improve quantify ability significantly. We have conducted an in depth set of empirical studies on a large scale net facial image test bed, within which encouraging results showed that the projected URL algorithms will considerably boost the performance of the promising SBFA theme. In future work we will use HAAR algorithm. HAAR is feature based method for face detection. HAAR features, integral images, recognized detection of features improve face detection in terms of speed and accuracy. DOI: 10.17762/ijritcc2321-8169.150517

Heap Abstractions for Static Analysis

Heap data is potentially unbounded and seemingly arbitrary. As a consequence, unlike stack and static memory, heap memory cannot be abstracted directly in terms of a fixed set of source variable names appearing in the program being analysed. This makes it an interesting topic of study and there is an abundance of literature employing heap abstractions. Although most studies have addressed similar concerns, their formulations and formalisms often seem dissimilar and some times even unrelated. Thus, the insights gained in one description of heap abstraction may not directly carry over to some other description. This survey is a result of our quest for a unifying theme in the existing descriptions of heap abstractions. In particular, our interest lies in the abstractions and not in the algorithms that construct them. In our search of a unified theme, we view a heap abstraction as consisting of two features: a heap model to represent the heap memory and a summarization technique for bounding the heap representation. We classify the models as storeless, store based, and hybrid. We describe various summarization techniques based on k-limiting, allocation sites, patterns, variables, other generic instrumentation predicates, and higher-order logics. This approach allows us to compare the insights of a large number of seemingly dissimilar heap abstractions and also paves way for creating new abstractions by mix-and-match of models and summarization techniques.Comment: 49 pages, 20 figure

Spiritual Immigration

The thesis presents my personal and external interpretations of people who have been directly or indirectly involved with the immigration process. First, each migration consists of unique elements based on the culture and circumstances of the immigrant. Personal experiences are important to the development of this theme. Individual perceptions of the sea as a migratory vehicle are used to interpret these unique elements. Second, the theme is developed from a humanistic viewpoint. Finally, theories such as the search for Utopia and historical events like the Cuban mass migration to the US are utilized to help understand the immigration process. These paintings were created to expose the viewer to this social issue, which continues to affect our society, as seen from my spiritual and humanistic perspective. My Technique pursues expressiveness through light, figurative forms, and color. Inspirations are expressed through integrating my use of the vocabulary of color with the representation of different immigrant experiences. The canvas measurements expand the vision of the theme, which is complemented by color iconography, and the specific sensitivity of each painting. These techniques help me explore and interpret a major socio-political issue of our society, immigration

Retrieval through explanation : an abductive inference approach to relevance feedback

Relevance feedback techniques are designed to automatically improve a system's representation of a query by using documents the user has marked as relevant. However, traditional relevance feedback models suffer from a number of limitations that restrict their potential in supporting information seeking. One of the major limitations of relevance feedback is that it does not incorporate behavioural aspects of information seeking - how and why users assess relevance. We propose that relevance feedback should be viewed as a process of explanation and demonstrate how this limitation of relevance feedback techniques can be overcome by a theory of relevance feedback based on abductive inference

Selective relevance feedback using term characteristics

This paper presents a new relevance feedback technique; selectively combining evidence based on the usage of terms within documents. By considering how terms are used within documents, we can better describe the features that might make a document relevant and thus improve retrieval effectiveness. In this paper we present an initial, experimental investigation of this technique, incorporating new and existing measures for describing the information content of a document. The results from these experiments positively support our hypothesis that extending relevance feedback to take into account how terms are used within documents can improve the performance of relevance feedback