[PDF] Top 20 A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

... Image acquisition and sharing facilities have fostered the creation of huge image collections. This scenario has demanded the development of effective systems to sup- port the search for relevant images, ... See full document

... of image low-level visual ...and semi-BDEE, they have compared them against the conventional RF algorithms and show a significant improvement in terms of accuracy and stability based on a subset of the ... See full document

... for relevance feedback and image matching in region level and improved the retrieval ...the image into main region and margin region based on high dimension biomimetic information ... See full document

... information retrieval systems is to adapt the system to the specific user preferences making more important weights or features that reflect the actual user needs in order to achieve higher ...define ... See full document

... hierarchical algorithm based on binary Bayesian ...the learning ability of classifier in true decision ...test image has tobe taken from one of the ... See full document

... for image retrieval, generally, the retrieval results based on the similarities of pure visual features are not necessarily perceptually and semantically ...of image property and it is usually ... See full document

... Discriminant analysis [18] is one of the most popular solu- tions for the small sample learning problem. In the last 20 years, Fisher linear discriminant analysis (LDA) has been successfully used in face ... See full document

... of image retrieval algorithm based on relevance feedback are determined in advance, which is lack of ...optimization algorithm is introduced into relevance ...QPSO ... See full document

... long-term relevance feedback (LRF) with HA to increase both efficiency and retrieval accuracy of CBIR ...concept, semi- supervised learning was incorporated to automatically ... See full document

... Content-based image retrieval refers to the use of image visual content, such as color, texture, shape, spatial layout, ...of image data search for the users which satisfied their ...[1], ... See full document

... Rank learning tasks are generally stated as optimization problems: to find the best model (or the best adjustment in a given model) according to some representation to rank ...rank learning normally apply a ... See full document

... and retrieval systems for health related biomedical ...Information Retrieval (BIR) seeks special atten- tion due to the characteristics of biomedical termi- ...of retrieval and ranking of documents ... See full document

...  Relevance feedback – is a technique that considers human interaction to refine high level concepts defined by low level ...based retrieval[5]. In the application of using relevance ... See full document

... supervised learning algorithmic rule mentioned to as SHG- Health for risk predictions to categorize increasingly developing scenario with the bulk of the information unlabeled Wide-ranging experiments ... See full document

... Relevance feedback (RF) is the retrieval task where the system is given not only a user query, but also user feedback on some of the top ranked ...results. Feedback gives the ... See full document

... of image and best describes the original ...the image has most of the energy conserved in the upper left-hand corner of the transform and consists primarily of values that are zero or near zero in the ... See full document

... concepts, relevance feedback was introduced into ...query image is De-noised using a non-linear filtering technique which is useful to find the edge of an image ...An image can be ... See full document

... maximization algorithm and clearly demonstrates the im- provement in query results attained by querying using region-based indexing features rather than global image properties, under the QbE ... See full document

... information retrieval models, notably statistical language models, assume a generative process of document creation, whereas the terms are chosen to be included in the document according to their importance to the ... See full document

... modal learning (MML) is therefore proposed to explicitly fuse the complementary information from different modalities to achieve improved ...view learning, and the relevant algorithms can be classified into ... See full document