Article thumbnail
Location of Repository

Generic Object Recognition Using Probabilistic-Based Semantic Component

By 吳家維 and Jia-Wei Wu

Abstract

[[abstract]]Object recognition based on semantic contents of images is more reasonable than that based on low-level image features. In order to bridge the semantic gap between low-level image features and high-level concepts in human cognition, we presents an unsupervised approach to build a new image representation, which is called probabilistic semantic component descriptor (pSCD), by collecting high-level concepts from images. We first quantize low-level features into a set of visual words, and then we apply a revised model of probabilistic Latent Semantic Analysis (pLSA) to analyze what kinds of hidden concepts between visual words and images are involved. After collecting these discovered concepts, we could build pSCD for object recognition. We also discuss how many hidden concepts are appropriate for pSCD to describe a set of images. Finally, through object recognition experiments, we demonstrate that pSCD is more discriminative than other image representations, including Bag-of-Words (BoW) and pLSA representations.

Topics: 物件辨識;語意隔閡;視覺字組;袋字模型;影像表示法, object recognition;semantic gap;visual word;bag-of-words model;image representation, [[classification]]42
Year: 2011
OAI identifier: oai:ir.lib.ntnu.edu.tw:309250000Q/74321
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://ir.lib.ntnu.edu.tw/ir/h... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.