Content Based Image Retrieval by Using Interactive Relevance Feedback Technique - A Survey

Due to rapid increase in storing and capturing multimedia data with the digital device, Content Based Image Retrieval play a very important role in the field of image processing. Although wide ranging studies have been done in the field of CBIR but image finding from multimedia data basis is still very complicated and open problem. If paper provide an review of CBIR based on some of the famous techniques such as Interactive Genetic Algorithm, Relevance Feedback (RS), Neural Network and so on. Relevance Feedback can be used to enhance the ability of CBIR effectively by dropping the semantic gap between low level feature and high level feature. Interactiveness on CBIR can also be done with the help of Genetic Algorithms. GA is the branch of evolutionary computation which makes the retrieval process more interactive so that user can get advanced results from database by comparing to Query Image with its evaluation. The result of traditional implicit feedback can also be improved by Neuro Fuzzy Logic based implicit feedback. This paper covers all the aspect of Relevance Feedback (RF), Interactive Genetic Algorithms, Neural Network in Content Based Image Retrieval, various RF techniques and applications of CBIR. DOI: 10.17762/ijritcc2321-8169.15075

Relevant clouds: leveraging relevance feedback to build tag clouds for image search

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40802-1_18Previous work in the literature has been aimed at exploring tag clouds to improve image search and potentially increase retrieval performance. However, to date none has considered the idea of building tag clouds derived from relevance feedback. We propose a simple approach to such an idea, where the tag cloud gives more importance to the words from the relevant images than the non-relevant ones. A preliminary study with 164 queries inspected by 14 participants over a 30M dataset of automatically annotated images showed that 1) tag clouds derived this way are found to be informative: users considered roughly 20% of the presented tags to be relevant for any query at any time; and 2) the importance given to the tags correlates with user judgments: tags ranked in the first positions tended to be perceived more often as relevant to the topic that users had in mind.Work supported by EU FP7/2007-2013 under grant agreements 600707 (tranScriptorium) and 287576 (CasMaCat), and by the STraDA project (TIN2012-37475-C02-01).Leiva Torres, LA.; Villegas Santamaría, M.; Paredes Palacios, R. (2013). Relevant clouds: leveraging relevance feedback to build tag clouds for image search. En Information Access Evaluation. Multilinguality, Multimodality, and Visualization. Springer Verlag (Germany). 143-149. https://doi.org/10.1007/978-3-642-40802-1_18S143149Begelman, G., Keller, P., Smadja, F.: Automated tag clustering: Improving search and exploration in the tag space. In: Collaborative Web Tagging (2006)Callegari, J., Morreale, P.: Assessment of the utility of tag clouds for faster image retrieval. In: Proc. MIR (2010)Ganchev, K., Hall, K., McDonald, R., Petrov, S.: Using search-logs to improve query tagging. In: Proc. ACL (2012)Hassan-Montero, Y., Herrero-Solana, V.: Improving tag-clouds as visual information retrieval interfaces. In: Proc. InSciT (2006)Leiva, L.A., Villegas, M., Paredes, R.: Query refinement suggestion in multimodal interactive image retrieval. In: Proc. ICMI (2011)Liu, D., Hua, X.-S., Yang, L., Wang, M., Zhang, H.-J.: Tag ranking. In: Proc. WWW (2009)Overell, S., Sigurbjörnsson, B., van Zwol, R.: Classifying tags using open content resources. In: Proc. WSDM (2009)Rui, Y., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance feedback: A power tool for interactive content-based image retrieval. T. Circ. Syst. Vid. 8(5) (1998)Sigurbjörnsson, B., van Zwol, R.: Flickr tag recommendation based on collective knowledge. In: Proc. WWW (2008)Trattner, C., Lin, Y.-L., Parra, D., Yue, Z., Real, W., Brusilovsky, P.: Evaluating tag-based information access in image collections. In: Proc. HT (2012)Villegas, M., Paredes, R.: Image-text dataset generation for image annotation and retrieval. In: Proc. CERI (2012)Zhang, C., Chai, J.Y., Jin, R.: User term feedback in interactive text-based image retrieval. In: Proc. SIGIR (2005

Interactive retrieval of video using pre-computed shot-shot similarities

A probabilistic framework for content-based interactive video retrieval is described. The developed indexing of video fragments originates from the probability of the user's positive judgment about key-frames of video shots. Initial estimates of the probabilities are obtained from low-level feature representation. Only statistically significant estimates are picked out, the rest are replaced by an appropriate constant allowing efficient access at search time without loss of search quality and leading to improvement in most experiments. With time, these probability estimates are updated from the relevance judgment of users performing searches, resulting in further substantial increases in mean average precision

Video information retrieval using objects and ostensive relevance feedback

In this paper, we present a brief overview of current approaches to video information retrieval (IR) and we highlight its limitations and drawbacks in terms of satisfying user needs. We then describe a method of incorporating object-based relevance feedback into video IR which we believe opens up new possibilities for helping users find information in video archives. Following this we describe our own work on shot retrieval from video archives which uses object detection, object-based relevance feedback and a variation of relevance feedback called ostensive RF which is particularly appropriate for this type of retrieval

The relationship between IR and multimedia databases

Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud \ud Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud \ud Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud \ud First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud \ud Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud \ud Third, we add the functionality to process the users' relevance feedback.\ud \ud We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud \ud We conclude with an outline for implementation of miRRor on top of the Monet extensible database system

Using segmented objects in ostensive video shot retrieval

This paper presents a system for video shot retrieval in which shots are retrieved based on matching video objects using a combination of colour, shape and texture. Rather than matching on individual objects, our system supports sets of query objects which in total reflect the user’s object-based information need. Our work also adapts to a shifting user information need by initiating the partitioning of a user’s search into two or more distinct search threads, which can be followed by the user in sequence. This is an automatic process which maps neatly to the ostensive model for information retrieval in that it allows a user to place a virtual checkpoint on their search, explore one thread or aspect of their information need and then return to that checkpoint to then explore an alternative thread. Our system is fully functional and operational and in this paper we illustrate several design decisions we have made in building it