Difficulties in Image Retrieval

The semantic gap is often regarded as a major problem in the field of image retrieval research. In this paper, I will show that there are other important topics that should be addressed for improving the image retrieval utility. Among them, the exploitation of limited information and motivating the use of images are considered to be central to the development of image retrieval

Finding relevant videos in big data environments - how to utilize graph processing systems for video retrieval

The fast growing amount of videos in the web arises new challenges. The first is to find relevant videos for specific queries. This can be addressed by Content Based Video Retrieval (CBVR), in which the video data is used to do retrieval. A second challenge is to perform such CBVR with big amounts of data. In this work both challenges are targeted by using a distributed Big Graph Processing System for CBVR. A graph framework for CBVR is built with Apache Giraph. The system is generic in regard of the used feature set. A similarity graph is built with the chosen features. The graph system provides a insert operation for adding new videos and a query operation for retrieval. The query uses a fast fuzzy search for seeds of a personalized Pagerank, which uses the locality of the similarity graph for improving the fuzzy search. The graph system is tested with SIFT features for object recognition and matching. In the evaluation the Stanford I2V is used

Aplicació rica d'internet per a la consulta amb text i imatge a la CCMA

Premi millor projecte final de carrera d’Enginyeria de Telecomunicació en Serveis Telemàtics. Atorgat per Accenture (Curs 2009-2010)Award-winnin

Aplicació rica d'internet per a la consulta amb text i imatge a la CCMA

Premi millor projecte final de carrera d’Enginyeria de Telecomunicació en Serveis Telemàtics. Atorgat per Accenture (Curs 2009-2010)Award-winnin

Difficulties in Image Retrieval

• My ideas on difficulties: 1. information scarcity 2. lack of motivation – Difficulties in MIR [jaimes 05] • query formulation, usage, and the semantic gap • different conclusion on image retrieval ¥sub MIR – Focuses on the effectiveness, not the efficiency or utility of image retrieval. 3. Motivating Users 1. We spend a lot of time watching videos, listening to music, but not much on images. – Why? – Because the power of images are not well recognized. 2. Further, people do not use image retrieval often. – Why? – Not because the technology isn’t good enough. – But because we do not know how we can benefit from image retrieval. 3.1 Power of Images • Are there anything that only images can do? – e.g., photo‐based QA • Text can express anything we can think of. – but there is not redundancy (unintended background) • Videos can do everything images can and more. – but it takes time to watch them – instruction video is easy to create and hard to follow (control of speed) Cultural reason (video vs. images