A Bayesian Framework for Recognizing Textured Objects in a Content- Based Image Retrieval System
نویسندگان
چکیده
We present an image retrieval system for finding textured objects by text query. The algorithm considers each image as a set of independent segments. It learns the relationship of segments features with text from a training set of images where a set of segments is manually labeled. The algorithm is capable of generating text labels from a segment and finding images that are relevant to a query consisting of text labels. We present the first experimental results.
منابع مشابه
Semiautomatic Image Retrieval Using the High Level Semantic Labels
Content-based image retrieval and text-based image retrieval are two fundamental approaches in the field of image retrieval. The challenges related to each of these approaches, guide the researchers to use combining approaches and semi-automatic retrieval using the user interaction in the retrieval cycle. Hence, in this paper, an image retrieval system is introduced that provided two kind of qu...
متن کاملImage segmentation and similarity of color-texture objects
We aim for content-based image retrieval of textured objects in natural scenes under varying illumination and viewing conditions. To achieve this, image retrieval is based on matching feature distributions derived from color invariant gradients. To cope with object cluttering, regionbased texture segmentation is applied on the target images prior to the actual image retrieval process. The retri...
متن کاملDocument Image Retrieval Based on Keyword Spotting Using Relevance Feedback
Keyword Spotting is a well-known method in document image retrieval. In this method, Search in document images is based on query word image. In this Paper, an approach for document image retrieval based on keyword spotting has been proposed. In proposed method, a framework using relevance feedback is presented. Relevance feedback, an interactive and efficient method is used in this paper to imp...
متن کاملImage retrieval using the combination of text-based and content-based algorithms
Image retrieval is an important research field which has received great attention in the last decades. In this paper, we present an approach for the image retrieval based on the combination of text-based and content-based features. For text-based features, keywords and for content-based features, color and texture features have been used. Query in this system contains some keywords and an input...
متن کاملBayesian representations and learning mechanisms for content-based image retrieval
We have previously introduced a Bayesian framework for content-based image retrieval (CBIR) that relies on a generative model for feature representation based on embedded mixtures. This is a truly generic image representation that can jointly model color and texture and has been shown to perform well across a broad spectrum of image databases. In this paper, we expand the Bayesian framework alo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2004