IJSRST162663 | Received: 01 Dec 2016 | Accepted: 08 Dec 2016 | November-December-2016 [(2)6: 340-342 ] © 2016 IJSRST | Volume 2 | Issue 6 | Print ISSN: 2395-6011 | Online ISSN: 2395-602X Themed Section: Engineering and Technology 340 Entropy Optimization for Image Retrieval Sona Rahim, Hafsath Ca Department of Computer Science & Engineering, Ilahia College of Engineering & Technology, Muvattupuzha, Kerala, India ABSTRACT Recent studies shows that there are several information retrieval are being developed. Earlier the main focus was text retrieval. Information retrieval (IR) concerned with the searching and retrieving of knowledge-based information from database. In this paper, we represent the various models and techniques for information retrieval. This paper focuses some of the most related image retrieval techniques. Keywords : Search And Retrieval, Dictionary Learning, BOW, Entropy Optimization, Image Retrieval. I. INTRODUCTION Information retrieval (IR) is the task of retrieving objects, like images, from a database where the user's information need. Research focused mostly on text retrieval ,now in image retrieval, and video retrieval , since the availability of digital technology led to a great increase of multimedia data Image retrieval is the most studied and challenging aspect of multimedia information retrieval. For image category classification Bag of words process is used,it is by extracting feature of images.Bag of words is a model that is used in natural language processing and information retrieval.The BoW model takes each image as a document ,it contains a number of different “visual” words.The features can be extracted by some clustering algorithms.From this bag of words(BOW) codebook is generated In BOW features are calculated in terms of frequency.Most popular ways to normalize the frequencies is to weight a term by inverse of document frequency(tf-idf).For classification purpose class label of a document is taken.Entropy is a key measure in Information retrieval,it quantifies the amount of uncertainty in values or the outcome of random process.Entropy refers to the disorder . The state-of-the art methods, b) reduce the storage requirements and query time by using smaller dictionaries, and c) transfer the learned knowledge to previously unseen classes without retraining. The EO-BoW, which optimizes a retrieval-oriented objective function. We demonstrated the ability of the proposed method to improve the retrieval performance using two image datasets, a collection of time-series datasets, a text dataset and a video dataset. II. METHODS AND MATERIAL Entropy optimization by bag of words, in this method bag of words alone cannot be used, it can be used for extracting a feature vector from each word of a text document .For optimizing the Entropy is opted ,inoder to maximize the relavant information[1].It is a effective retrieval dictionary learning method to improve retrieval precision beyond other methods and can reduce storage space and time .Without training learned dictionary can be transfered.By using entropy codebook can be optimized.By this method images can be retrieved and can be used in other retrieval. Spatial Pyramid Matching for categorization,in this paper the semantic category of the image is resolved by spatial pyramid matching method.It a method for effective scene categorization[2].It produce high accuracy on large database. Build pyramid in image space, quantize feature space is the approach used. Find maximum-weight matching (weight is inversely proportional to distance). Global spatial regularities (natural scene statistics) help even in databases with high geometric variability.