HierarSearch:Enhancing Performance of Search Engines by Mining Semantic Relationships Among Results

Web has a plethora of information.Modern search engines(such Google,Bing)can retrieve web pages based on keywords and return them in a ranking list.However,users with little knowledge in a certain domain would have difficulty in finding appropriate keywords to retrieve the web pages they intend to find.What’s more,little interaction with search engine restrains users to do further investigation among the results.If users want to refine their queries,no assistance about potential related concepts/instances is provided.In this paper,we introduce a new search engine:HierarSearch,which enhances performance of search engines by providing an interactive keyword hierarchy generated from webpages retrieved.Users can interact with the tag cloud keyword hierarchy to know the semantic relationships among results,and also refine queries by selecting keywords in tag cloud.HierarSearch would generate refined keywords query“intelligently”with the help of a very large database,which understands human world.

Palmprint Phenotype Feature Extraction and Classification Based on Deep Learning

Palmprints are of long practical and cultural interest.Palmprint principal lines,also called primary palmar lines,are one of the most dominant palmprint features and do not change over the lifespan.The existing methods utilize filters and edge detection operators to get the principal lines from the palm region of interest(ROI),but can not distinguish the principal lines from fine wrinkles.This paper proposes a novel deep-learning architecture to extract palmprint principal lines,which could greatly reduce the influence of fine wrinkles,and classify palmprint phenotypes further from 2D palmprint images.This architecture includes three modules,ROI extraction module(REM)using pre-trained hand key point location model,principal line extraction module(PLEM)using deep edge detection model,and phenotype classifier(PC)based on ResNet34 network.Compared with the current ROI extraction method,our extraction is competitive with a success rate of 95.2%.For principal line extraction,the similarity score between our extracted lines and ground truth palmprint lines achieves 0.813.And the proposed architecture achieves a phenotype classification accuracy of 95.7%based on our self-built palmprint dataset CAS_Palm.