ABSTRACT:
Scalable image search based on visual similarity has been an active topic of research in recent years. State-of-the-art solutions often use hashing methods to embed high-dimensional image features into Hamming space, where search can be performed in real-time based on Hamming distance of compact hash codes. Unlike traditional metrics (e.g., Euclidean) that offer continuous distances, the Hamming distances are discrete integer values. As a consequence, there are often a large number of images sharing equal Hamming distances to a query, which largely hurts search results where fine-grained ranking is very important. This paper introduces an approach that enables query-adaptive ranking of the returned images with equal Hamming distances to the queries. This is achieved by firstly offline learning bitwise weights of the hash codes for a diverse set of predefined semantic concept classes. We formulate the weight learning process as a quadratic programming problem that minimizes intra-class distance while preserving inter-class relationship captured by original raw image features. Query-adaptive weights are then computed online by evaluating the proximity between a query and the semantic concept classes. With the query-adaptive bitwise weights, returned images can be easily ordered by weighted Hamming distance at a finer-grained hash code level rather than the original Hamming distance level. Experiments on a Flickr image dataset show clear improvements from our proposed approach.

EXISTING SYSTEM:

While traditional image search engines heavily rely on textual words associated to the images, scalable content-based search is receiving increasing attention. Apart from providing better image search experience for ordinary Web users, large-scale similar image search has also been demonstrated to be very helpful for solving a number of very hard problems in computer vision and multimedia such as image categorization.

DISADVANTAGES OF EXISTING SYSTEM:
An efficient search mechanism is critical since existing image features are mostly of high dimensions and current image databases are huge, on top of which exhaustively comparing a query with every database sample is computationally prohibitive.

PROPOSED SYSTEM:
In this work we represent images using the popular bag-of-visual-words (BoW) framework, where local invariant image descriptors (e.g., SIFT) are extracted and quantized based on a set of visual words. The BoW features are then embedded into compact hash codes for efficient search. For this, we consider state-of-the-art techniques including semi-supervised hashing and semantic hashing with deep belief networks. Hashing is preferable over tree-based indexing structures (e.g., kd-tree) as it generally requires greatly reduced memory and also works better for high-dimensional samples. With the hash codes, image similarity can be efficiently measured (using logical XOR operations) in Hamming space by Hamming distance, an integer value obtained by counting the number of bits at which the binary values are different. In large scale applications, the dimension of Hamming space is usually set as a small number (e.g., less than a hundred) to reduce memory cost and avoid low recall.

ADVANTAGES OF PROPOSED SYSTEM:
The main contribution of this paper is the proposal of a novel approach that computes query-adaptive weights for each bit of the hash codes, which has two main advantages. First, images can be ranked on a finer-grained hash code level since—with the bitwise weights—each hash code is expected to have a unique similarity to the queries. In other words, we can push the resolution of ranking from (traditional Hamming distance level) up to (hash code level1). Second, contrary to using a single set of weights for all the queries, our approach tailors a different and more suitable set of weights for each query.

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