Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Extending beyond the boundaries of science, art, and culture, content-based multimedia information retrieval provides new paradigms and methods for searching through the myriad variety of media all over the world. This survey reviews 100p recent articles on content-based multimedia information retrieval and discusses their role in current research directions which include browsing and search paradigms, user studies, affective computing, learning, semantic queries, new features and media types, high performance indexing, and evaluation techniques. Based on the current state of the art, we discuss the major challenges for the future.

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Content-based multimedia information retrieval: State of the art and challenges

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

Cognitive engineering

Dempster-Shafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. Dempster-Shafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. This is a potentially valuable tool for the evaluation of risk and reliability in engineering applications when it is not possible to obtain a precise measurement from experiments, or when knowledge is obtained from expert elicitation. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them. This report surveys a number of possible combination rules for Dempster-Shafer structures and provides examples of the implementation of these rules for discrete and interval-valued data.

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Combination of Evidence in Dempster-Shafer Theory

Rapid advances in sensor, storage, processor, and communication technologies let consumers store large digital photo collections. Consumers need effective tools to organize and access photos in a semantically meaningful way. We address the semantic gap between feature-based indexes computed automatically and human query and retrieval preferences.

Home photo content modeling for personalized event-based retrieval

In this paper, we present a system for the image indexing and retrieval using speech annotations based on a pre-defined structured syntax. In addition to the introduction of N-best lists for index generation, a query expansion technique is explored to enhance the query terms and to improve retrieval effectiveness. By adding the most probable substitutions for the query terms, more relevant images are distinguished from the data collection. This approach is particularly helpful to deal with those less frequently used words, including out-of-vocabulary (OOV) words, which are very common for names of people and places. Experiments on a collection of 1,200 photos show that the retrieval effectiveness is increased considerably for segment of individual domain on People, Location and Event. With this method, the average value of precision versus recall over a combination of segments has improved significantly, from 50% to 72.4%.

An Improved Method for Image Retrieval using Speech Annotation

An audio-mixing artist usually adds the musical accompaniment to video. Employing such artists is expensive and not feasible for a home video presentation. Our automatic audio-video mixing technique is suited for home videos. It uses a pivot vector space mapping method that matches video shots with music segments based on aesthetic cinematographic heuristics.

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Pivot vector space approach for audio-video mixing

This article compares eight different diversity methods: 3 based on visual information, 1 based on date information, 3 adapted to each topic based on location and visual information; finally, for completeness, 1 based on random permutation To compare the effectiveness of these methods, we apply them on 26 runs obtained with varied methods from different research teams and based on different modalities We then discuss the results of the more than 200 obtained runs The results show that query-adapted methods are more effcient than nonadapted method, that visual only runs are more difficult to diversify than text only and text-image runs, and finally that only few methods maximize both the precision and the cluster recall at 20 documents

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A comparative study of diversity methods for hybrid text and image retrieval approaches

Conceptual graphs are very useful for representing structured knowledge. However, existing formulations of fuzzy conceptual graphs are not suitable for matching images of natural scenes. This paper presents a new variation of fuzzy conceptual graphs that is more suited to image matching. This variant differentiates between a model graph that describes a known scene and an image graph which describes an input image. A new measurement is defined to measure how well a model graph matches an image graph. A fuzzy graph matching algorithm is developed based on error-tolerant subgraph isomorphism. Test results show that the matching algorithm gives very good results for matching images to predefined scene models.

Philippe Mulhem

Papers

Home photo content modeling for personalized event-based retrieval

An Improved Method for Image Retrieval using Speech Annotation

Pivot vector space approach for audio-video mixing

A comparative study of diversity methods for hybrid text and image retrieval approaches

Fuzzy conceptual graphs for matching images of natural scenes