PPT-Describable Visual Attributes for Face Verification and Image Search

Neeraj Kumar Alexander C Berg Peter N Belumeur and Shree K Nayar Presented by Gregory Teodoro Attribute Classification Early research focused on gender and ethnicity Done on small datasets. stonybrookedu Abstract With the rise in popularity of digital cameras the amount of visual data available on the web is growing exponen tially Some of these pictures are extremely beautiful and aesthetically pleasing but the vast majority are uninter stonybrookedu Abstract With the rise in popularity of digital cameras the amount of visual data available on the web is growing exponen tially Some of these pictures are extremely beautiful and aesthetically pleasing but the vast majority are uninter Attributes are visual qualities of objects such as red striped or spotted The model sees attributes as patterns of image segments repeatedly sharing some characteristic properties These can be any combination of appearance shape or the layout of seg State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Consumer application: Apple . iPhoto. http://www.apple.com/ilife/iphoto/. Tech Talk @ . shutterstock. . . Presenter: . Chen-Ping Yu, PhD Candidate. Research Advisors: . Dr. . Dimitris. Samaras (computer science). Dr. Greg . Zelinsky. (psychology). . . State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Consumer application: Apple . iPhoto. http://www.apple.com/ilife/iphoto/. Image Search Results. for Synonymous Queries. Nate . Stender. , Dr. Lu. The Problem. There are . billions of images . on the internet.. When we search for an image we expect a result with . relevant images. Andrew Chi. Brian Cristante. COMP 790-133: January 27, 2015. Image Retrieval. AI / Vision Problem. Systems Design / Software Engineering Problem. Sensory Gap. : “What features should we use?”. Query-Dependent?. Tech Talk @ . shutterstock. . . Presenter: . Chen-Ping Yu, PhD Candidate. Research Advisors: . Dr. . Dimitris. Samaras (computer science). Dr. Greg . Zelinsky. (psychology). . . Kaushik . Nandan. 1. Contents:. Introduction. Related . Work. Segmentation as Selective . Search. Object Recognition . System. Evaluation. Conclusions. References. 2. 1. Introduction. Object recognition: determining . The call will begin at approximately 11am ET and run for approximately 1 hour. . The line will remain silent until the start of the call. . All lines will be muted. Any questions can by submitted via the Q&A panel in top of the menu of the application. . Kaushik . Nandan. 1. Contents:. Introduction. Related . Work. Segmentation as Selective . Search. Object Recognition . System. Evaluation. Conclusions. References. 2. 1. Introduction. Object recognition: determining . The eye and the mind. The eye and the mind. How does the eye work?. The eye and the mind. But the eye is not that perfect. • Upside down images.. • Curved lines.. • Blind spot.. The eye and the mind. Linda Shapiro. ECE P 596. 1. What’s Coming. Review of . Bakic. flesh . d. etector. Fleck and Forsyth flesh . d. etector. Review of Rowley face . d. etector. Overview of. . Viola Jones face detector with .